BDA3_notes.Rmd

---
title: "Bayesian Data Analysis course - BDA3 notes"
date: "Page updated: `r format.Date(file.mtime('BDA3_notes.Rmd'),'%Y-%m-%d')`"
editor_options: 
  markdown: 
    wrap: 72
---

\newcommand{\E}{\operatorname{E}}
\newcommand{\N}{\operatorname{N}}
\newcommand{\Pr}{\operatorname{Pr}}
\newcommand{\Beta}{\operatorname{Beta}}
\newcommand{\Bin}{\operatorname{Bin}}
\newcommand{\BetaBinomial}{\operatorname{Beta-Binomial}}
\newcommand{\Invchisqr}{\operatorname{Inv-\chi^2}}
\newcommand{\NInvchisqr}{\operatorname{N-Inv-\chi^2}}
\newcommand{\tr}{\operatorname{tr}}
\newcommand{\trace}{\operatorname{trace}}
\newcommand{\logit}{\operatorname{logit}}

These notes help you to focus on the most important parts of each
chapter related o the Bayesian Data Analysis course. Before reading a
chapter, you can check below which sections, pages, and terms are the
most important. After reading the chapter or following the
corresponding lecture, you can check here for additional
clarifications. There also some notes for the chapters not included in
the course.

## Chapter 1 Probability and inference {#ch1}

[Chapter 1](https://users.aalto.fi/~ave/BDA3.pdf#page=13) is related to the pre-requisites and Lecture 1 *Introduction*.

### Outline

- 1.1-1.3 important terms, especially 1.3 for the notation
- 1.4 an example related to the first exercise, and another practical
    example
- 1.5 foundations
- 1.6 good example related to visualization exercise
- 1.7 example which can be skipped
- 1.8 background material, good to read before doing the first
    assignment
- 1.9 background material, good to read before doing the second
    assignment
- 1.10 a point of view for using Bayesian inference

### The most important terms

Find all the terms and symbols listed below. Note that some of the terms
are now only briefly introduced and will be covered later in more
detail. When reading the chapter, write down questions related to things
unclear for you or things you think might be unclear for others.

- full probability model
- posterior distribution
- potentially observable quantity
- quantities that are not directly observable
- exchangeability
- independently and identically distributed
- $\theta, y, \tilde{y}, x, X, p(\cdot|\cdot), p(\cdot), \Pr(\cdot), \sim, H$
- sd, E, var
- Bayes rule
- prior distribution
- sampling distribution, data distribution
- joint probability distribution
- posterior density
- probability
- density
- distribution
- $p(y|\theta)$ as a function of $y$ or $\theta$
- likelihood
- posterior predictive distribution
- probability as measure of uncertainty
- subjectivity and objectivity
- transformation of variables
- simulation
- inverse cumulative distribution function

### Recommended exercises

Optional but recommended end of the chapter exercises in BDA3 to get a better understanding of the chapter topic:

- [1.1-1.4, 1.6-1.8](https://users.aalto.fi/~ave/BDA3.pdf#page=37) ([model solutions available for 1.1-1.6](http://www.stat.columbia.edu/~gelman/book/solutions3.pdf))

### Distributed as, $\sim$

It is common to write statistical models using a following notation:
$$
\begin{aligned}
y & \sim \mathrm{normal}(\mu, \sigma) \\
\mu & \sim \mathrm{normal}(0, 10) \\
\sigma & \sim \mathrm{normal}^+(0, 1),
\end{aligned}
$$
where the symbol $\sim$ is called *tilde* (`\sim` in LateX). 
In general, we can read $\sim$ as *"is distributed as,"* and overall
this notation is used as a shorthand for defining distributions, so
that the above example can be written (with also as
$$
\begin{aligned}
   p(y| \mu, \sigma) & = \mathrm{normal}(y |  \mu, \sigma)\\
   p(\mu) & = \mathrm{normal}(\mu |  0, 10)\\
   p(\sigma) & = \mathrm{normal}^+(\sigma |  0, 1).
\end{aligned}
$$

A collection of distribution statements define a joint
distribution as the product of component distributions
$$
p(y,\mu,\sigma) = p(y| \mu, \sigma )p(\mu) p(\sigma).
$$

### Proportional to, $\propto$

The symbol $\propto$ means *proportional to*, which means left hand side
is equal to right hand size given a constant multiplier. For instance if
$y=2x$, then $y \propto x$. It's `\ propto` in LaTeX. See [Proportionality in Wikipedia](https://en.wikipedia.org/wiki/Proportionality_(mathematics)).

The Bayes rule is
$$
p(\theta | y) = \frac{p(y | \theta)p(\theta)}{p(y)},
$$
where dividing by $p(y)$ makes $\int p(\theta | y) d\theta = 1$. $p(y)$ is often infeasible to compute, but luckily we also often don't need to know it, and then we write the Bayes rule as
$$
p(\theta | y) \propto p(y | \theta)p(\theta).
$$

### Model and likelihood

Term $p(y|\theta,M)$ has two different names depending on the situation.
Due to the short notation used, there is possibility of confusion.

-   Term $p(y|\theta,M)$ is called a *model* (sometimes more
    specifically *observation model* or *statistical model*) when it is
    used to describe uncertainty about $y$ given $\theta$ and $M$.
    Longer notation $p_y(y|\theta,M)$ shows explicitly that it is a
    function of $y$.
-   In Bayes rule, the term $p(y|\theta,M)$ is called *likelihood
    function*. Posterior distribution describes the probability (or
    probability density) for different values of $\theta$ given a fixed
    $y$, and thus when the posterior is computed the terms on the right
    hand side (in Bayes rule) are also evaluated as a function of
    $\theta$ given fixed $y$. Longer notation $p_\theta(y|\theta,M)$
    shows explicitly that it is a function of $\theta$. Term has it's
    own name (likelihood) to make the difference to the model. The
    likelihood function is unnormalized probability distribution
    describing uncertainty related to $\theta$ (and that's why Bayes
    rule has the normalization term to get the posterior distribution).

### Two types of uncertainty

Epistemic and aleatory uncertainty are reviewed nicely in the article:
[Tony O'Hagan, ``Dicing with the unknown'' Significance 1(3):132-133,
2004.](http://onlinelibrary.wiley.com/doi/10.1111/j.1740-9713.2004.00050.x/abstract)

In that paper, there is one typo using the word *aleatory* instead of
*epistemic* (if you notice this, it's then quite obvious).

### Transformation of variables

- See [BDA3 p. 21](https://users.aalto.fi/~ave/BDA3.pdf#page=31)

### Ambiguous notation in statistics

- In $p(y|\theta)$
    - $y$ can be variable or value
        - we could clarify by using $p(Y|\theta)$ or $p(y|\theta)$
    - $\theta$ can be variable or value
        - we could clarify by using $p(y|\Theta)$ or $p(y|\theta)$
    - $p$ can be a discrete or continuous function of $y$ or $\theta$
        - we could clarify by using $P_Y$, $P_\Theta$, $p_Y$ or
            $p_\Theta$
    - $P_Y(Y|\Theta=\theta)$ is a probability mass function, sampling
        distribution, observation model
    - $P(Y=y|\Theta=\theta)$ is a probability
    - $P_\Theta(Y=y|\Theta)$ is a likelihood function (can be discrete
        or continuous)
    - $p_Y(Y|\Theta=\theta)$ is a probability density function,
        sampling distribution, observation model
    - $p(Y=y|\Theta=\theta)$ is a density
    - $p_\Theta(Y=y|\Theta)$ is a likelihood function (can be discrete
        or continuous)
    - $y$ and $\theta$ can also be mix of continuous and discrete
    - Due to the sloppiness sometimes likelihood is used to refer
        $P_{Y,\theta}(Y|\Theta)$, $p_{Y,\theta}(Y|\Theta)$

### Exchangeability

You don't need to understand or use the term exchangeability before
Chapter 5 and Lecture 7. At this point and until Chapter 5 and Lecture
7, it is sufficient that you know that 1) independence is stronger
condition than exchangeability, 2) independence implies exchangeability,
3) exchangeability does not imply independence, 4) exchangeability is
related to what information is available instead of the properties of
unknown underlying data generating mechanism. If you want to know more
about exchangeability right now, then read BDA Section 5.2 and
[notes for Chapter 5](#ch5).

## Chapter 2 Single-parameter models {#ch2}

[Chapter 2](https://users.aalto.fi/~ave/BDA3.pdf#page=39) is related to the prerequisites and Lecture 2 *Basics of Bayesian inference*.

### Outline

- 2.1 Binomial model (e.g. biased coin flipping)
- 2.2 Posterior as compromise between data and prior information
- 2.3 Posterior summaries
- 2.4 Informative prior distributions (skip exponential families and
    sufficient statistics)
- 2.5 Gaussian model with known variance
- 2.6 Other single parameter models
    - in this course the normal distribution with known mean but
        unknown variance is the most important
    - glance through Poisson and exponential
- 2.7 glance through this example, which illustrates benefits of prior
    information, no need to read all the details (it's quite long
    example)
- 2.8 Noninformative priors
- 2.9 Weakly informative priors

Laplace's approach for approximating integrals is discussed in more
detail in Chapter 4.

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others.

- binomial model
- Bernoulli trial
- $\mathop{\mathrm{Bin}}$, $\binom{n}{y}$
- Laplace's law of succession
- think which expectations in eqs. 2.7-2.8
- summarizing posterior inference
- mode, mean, median, standard deviation, variance, quantile
- central posterior interval
- highest posterior density interval / region
- uninformative / informative prior distribution
- principle of insufficient reason
- hyperparameter
- conjugacy, conjugate family, conjugate prior distribution, natural
    conjugate prior
- nonconjugate prior
- normal distribution
- conjugate prior for mean of normal distribution with known variance
- posterior for mean of normal distribution with known variance
- precision
- posterior predictive distribution
- normal model with known mean but unknown variance
- proper and improper prior
- unnormalized density
- difficulties with noninformative priors
- weakly informative priors

### R and Python demos

- 2.1: Binomial model and Beta posterior. [R](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch2/demo2_1.ipynb).
- 2.2: Comparison of posterior distributions with different
    parameter values for the Beta prior distribution. [R](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_2.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch2/demo2_2.ipynb).
- 2.3: Use samples to plot histogram with quantiles, and the same
    for a transformed variable. [R](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_3.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch2/demo2_3.ipynb).
- 2.4: Grid sampling using inverse-cdf method. [R](https://avehtari.github.io/BDA_R_demos/demos_ch2/demo2_4.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch2/demo2_4.ipynb).

### Recommended exercises

Optional but recommended end of the chapter exercises in BDA3 to get a better understanding of the chapter topic:

- [2.1-2.5, 2.8, 2.9, 2.14, 2.17, 2.22](https://users.aalto.fi/~ave/BDA3.pdf#page=67) ([model solutions available for
  2.1-2.5, 2.7-2.13, 2.16, 2.17,
  2.20](http://www.stat.columbia.edu/~gelman/book/solutions3.pdf),
  and 2.14 is in course slides)

### Posterior, credible, and confidence intervals

- *Confidence interval* is used in frequentist statistics and not used
  in Bayesian statistics, but we mention it here to make that fact
  explicit. Given a confidence level \gamma$ (95% and 99% are typical
  values), a *confidence interval* is a random interval which contains
  the parameter being estimated $\gamma$\% of the time ([Wikipedia](https://en.wikipedia.org/wiki/Confidence_interval)).

- *Credible interval* is used in Bayesian statistics (and by choice
  the acronym is CI as for the confidence interval). *Credible
  interval* is defined such that an unobserved parameter value has a
  particular probability $\alpha$ to fall within it
  ([Wikipedia](https://en.wikipedia.org/wiki/Credible_interval)).
  *Credible interval* can be used to characterize any probability
  distribution.

- *Posterior interval* is a credible interval specifically
   characterizing posterior distribution.

### Integration over Beta distribution

Chapter 2 has an example of analysing the ratio of girls born in Paris
1745--1770. Laplace used binomial model and uniform prior which produces
Beta distribution as posterior distribution. Laplace wanted to calculate
$p(\theta \geq 0.5)$, which is obtained as
$$
\begin{aligned}
  p(\theta \geq 0.5) &=& \int_{0.5}^1  p(\mathbf{\theta}|y,n,M) d\theta \\
  &=& \frac{493473!}{241945!251527!} \int_{0.5}^1 \theta^y(1-\theta)^{n-y} d\theta\end{aligned}
$$
Note that $\Gamma(n)=(n-1)!$. Integral has a form which is called
*incomplete Beta function*. Bayes and Laplace had difficulties in
computing this, but nowadays there are several series and continued
fraction expressions. Furthermore usually the normalization term is
computed by computing $\log(\Gamma(\cdot))$ directly without explicitly
computing $\Gamma(\cdot)$. Bayes was able to solve integral given small
$n$ and $y$. In case of large $n$ and $y$, Laplace used Gaussian
approximation of the posterior (more in Chapter 4). In this specific
case, R `pbeta` gives the same results as Laplace's result with at least 3
digit accuracy.

### Numerical accuracy

Laplace calculated
$$
p(\theta \geq 0.5 | y, n, M) \approx 1.15 \times 10^{-42}.
$$
Correspondingly Laplace could have calculated
$$
p(\theta \geq 0.5 | y, n, M) = 1 - p(\theta \leq 0.5 | y, n, M),
$$
which in theory could be computed in R with
`1-pbeta(0.5,y+1,n-y+1)`.
In practice this fails, due to the limitation in the floating point
representation used by the computers. In R the largest floating point
number which is smaller than 1 is about 1-eps/4, where eps is about
$2.22 \times 10^{-16}$ (the smallest floating point number larger than 1
is 1+eps). Thus the result from `pbeta(0.5,y+1,n-y+1)` will be rounded
to 1 and $1-1=0\neq 1.15
\times 10^{-42}$. We can compute $p(\theta \geq 0.5 | y, n, M)$ in R
with `pbeta(0.5, y+1, n-y+1, lower.tail=FALSE)`.

### Highest Posterior Density interval

HPD interval is not invariant to reparametrization. Here's an
illustrative example (using R and package `HDInterval`):
```
> r <- exp(rnorm(1000))
> quantile(log(r),c(.05, .95))
       5%       95% 
-1.532931  1.655137 
> log(quantile(r,c(.05, .95)))
       5%       95% 
-1.532925  1.655139 
> hdi(log(r), credMass = 0.9)
    lower     upper 
-1.449125  1.739169 
attr(,"credMass")
[1] 0.9
> log(hdi(r, credMass = 0.9))
    lower     upper 
-2.607574  1.318569 
attr(,"credMass")
[1] 0.9
```

### Gaussian distribution in more complex models and methods

Gaussian distribution is commonly used in mixture models, hierarchical
models, hierarchical prior structures, scale mixture distributions,
Gaussian latent variable models, Gaussian processes, Gaussian random
Markov fields, Kalman filters, proposal distribution in Monte Carlo
methods, etc.

### Predictive distribution

Often the predictive distribution is more interesting than the posterior
distribution. The posterior distribution describes the uncertainty in
the parameters (like the proportion of red chips in the bag), but the
predictive distribution describes also the uncertainty about the future
event (like which color is picked next). This difference is important,
for example, if we want to what could happen if some treatment is given
to a patient.

In case of Gaussian distribution with known variance $\sigma^2$ the
model is
$$
\begin{aligned}
  y\sim \N(\theta,\sigma^2),
\end{aligned}
$$
where
$\sigma^2$ describes aleatoric uncertainty. Using uniform prior the
posterior is
$$
\begin{aligned}
  p(\theta|y) \sim \mathop{\mathrm{N}}(\theta|\bar{y},\sigma^2/n),
\end{aligned}
$$
where $\sigma^2/n$ described epistemic uncertainty related to $\theta$.
Using uniform prior the posterior predictive distribution for new
$\tilde{y}$ is
$$
\begin{aligned}
  p(\tilde{y}|y) \sim \N(\tilde{y}|\bar{y},\sigma^2+\sigma^2/n),
\end{aligned}
$$
where the uncertainty is sum of epistemic ($\sigma^2/n$) and aleatoric
uncertainty ($\sigma^2$).

### Non-informative and weakly informative priors

Our thinking has advanced since sections 2.8 and 2.9 were written. We're
even more strongly in favor weakly informative priors, and in favor of
more information in the priors. Non-informative priors are likely to
produce more unstable estimates (higher variance), and the lectures
include also examples of how seemingly non-informative priors can
actually be informative on some aspect. See further discussion and
example in the [Prior Choice Recommendations Wiki](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations).
Thus Prior Choice Recommendations Wiki will see also some further updates (we're doing
research and learning more all the time).

### Should we worry about rigged priors?

[Andrew Gelman's blog post answering worries that data analyst would
choose a too optimistic prior](http://andrewgelman.com/2017/10/04/worry-rigged-priors/).

### Prior knowledge elicitation

Prior elicitation transforms domain knowledge of various kinds into well-defined prior distributions. There are challenges in how to gather domain knowledge and hopw to transform that to mathematical form. We come back to the topic later in the course. A recent review [*"Prior Knowledge Elicitation: The Past, Present, and Future"* by Mikkola et al. (2023)](https://doi.org/10.1214/23-BA1381) provides more information for those interested to go beoyond the material in thos course.

### Exchangeability

You don't need to understand or use the term exchangeability before
Chapter 5 and Lecture 7. At this point and until Chapter 5 and Lecture
7, it is sufficient that you know that 1) independence is stronger
condition than exchangeability, 2) independence implies exchangeability,
3) exchangeability does not imply independence, 4) exchangeability is
related to what information is available instead of the properties of
unknown underlying data generating mechanism. If you want to know more
about exchangeability right now, then read BDA3 Section 5.2 and
[notes for Chapter 5](#ch5).

### The number of left-handed students in the class

- What we know and don't know
    - $N=L+R$ is the total number of students in the lecture hall,
      $N$ is known in the beginning
    - $L$ and $R$ are the number of left and right handed students, not known before we start asking
    - $n=l+r$ is the total number of students we have asked
    - $l$ and $r$ are the numbers of left and right handed students from the students we asked
    - we also know that $l \leq L \leq (N-r)$ and $r \leq R \leq (N-l)$
- After observing $n$ students with $l$ left handed, what we know about $L$?
    - We define $L=l+\tilde{l}$, where $\tilde{l}$ is the unobserved number of left handed students among those who we did not yet ask
    - Posterior distribution for $\theta$ is $\Beta(\alpha+l, \beta+r)$
    - Posterior predictive distribution for $\tilde{l}$ is
    $\BetaBinomial(\tilde{l} | N-n, \alpha+l, \beta+r)=\int_0^1\Bin(\tilde{l} | N-n, \theta)\Beta(\theta | \alpha+l, \beta+r)d\theta$
- Eventually as we have asked everyone, $n=N$, and there is no uncertainty on the number of left-handed students present, and $l=L$ and $\tilde{l}=0$. There is still uncertainty about $\theta$, but that is relevant only if we would like to make predictions beyond the students in the lecture hall.

## Chapter 3 Introduction to multiparameter models {#ch3}

[Chapter 3](https://users.aalto.fi/~ave/BDA3.pdf#page=73) is related to the Lecture 3 *Multidimensional posterior*.

### Outline

- 3.1 Marginalisation
- 3.2 Normal distribution with a noninformative prior (very important)
- 3.3 Normal distribution with a conjugate prior (very important)
- 3.4 Multinomial model (can be skipped)
- 3.5 Multivariate normal with known variance (needed later)
- 3.6 Multivariate normal with unknown variance (glance through)
- 3.7 Bioassay example (very important, related to one of the
    exercises)
- 3.8 Summary (summary)

Normal model is used a lot as a building block of the models in the
later chapters, so it is important to learn it now. Bioassay example is
good example used to illustrate many important concepts and it is used
in several exercises over the course.

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others.

- marginal distribution/density
- conditional distribution/density
- joint distribution/density
- nuisance parameter
- mixture
- normal distribution with a noninformative prior
- normal distribution with a conjugate prior
- sample variance
- sufficient statistics
- $\mu$, $\sigma^2$, $\bar{y}$, $s^2$
- a simple normal integral
- $\Invchisqr$
- factored density
- $t_{n-1}$
- degrees of freedom
- posterior predictive distribution
- $\NInvchisqr$
- variance matrix $\Sigma$
- nonconjugate model
- generalized linear model
- binomial model
- logistic transformation
- density at a grid

### R and Python demos

- 3.1: Visualize joint density and marginal densities of posterior
    of normal distribution with unknown mean and variance. [R](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch3/demo3_1.ipynb).
- 3.2: Visualize factored sampling and corresponding marginal and
    conditional density. [R](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_2.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch3/demo3_2.ipynb).
- 3.3: Visualize marginal distribution of $\mu$ as a mixture of
    normals. [R](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_3.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch3/demo3_3.ipynb).
- 3.4: Visualize sampling from the posterior predictive
    distribution. [R](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_4.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch3/demo3_4.ipynb).
- 3.5: Visualize Newcomb's data. [R](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_5.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch3/demo3_5.ipynb).
- 3.6: Visualize posterior in bioassay example. [R](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_6.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch3/demo3_6.ipynb).

### Recommended exercises

Optional but recommended end of the chapter exercises in BDA3 to get a better understanding of the chapter topic:

- [3.2, 3.3, 3.9](https://users.aalto.fi/~ave/BDA3.pdf#page=89) (model solutions available for 3.1-3.3, 3.5, 3.9, 3.10)

### Conjugate prior for normal distribution

[BDA3 p. 67](https://users.aalto.fi/~ave/BDA3.pdf#page=77)
mentions that the conjugate prior for normal
distribution has to have a product form $p(\sigma^2)p(\mu|\sigma^2)$.
The book refers to (3.2) and the following discussion. As additional
hint is useful to think the relation of terms $(n-1)s^2$ and
$n(\bar{y}-\mu)^2$ in 3.2 to equations 3.3 and 3.4.

### Trace of square matrix

Trace of square matrix, $\trace$,
$\tr A$, $\trace(A)$,
$\tr(A)$, is the sum of diagonal elements. To derive
equation 3.11 the following property has been used
$\tr(ABC) = \tr(CAB) = \tr(BCA)$.

### History and naming of distributions

See [Earliest Known Uses of Some of the Words of Mathematics](http://jeff560.tripod.com/mathword.html).

### Using Monte Carlo to obtain draws from the posterior of generated quantities

Chapter 3 discusses closed form posteriors for binomial and normal
models given conjugate priors. These are also used as part of the
assignment. The assignment also requires forming a posterior for derived
quantities, and these posterior don't have closed form (so no need to
try derive them). As we know how to sample from the posterior of
binomial and normal models, we can use these posterior draws to get
draws from the posterior of derived quantity.

For example, given posteriors $p(\theta_1|y_1)$ and $p(\theta_2|y_2)$ we
want to find the posterior for the difference
$p(\theta_1-\theta_2|y_1,y_2)$.

1.  Sample $\theta_1^s$ from $p(\theta_1|y_1)$ and $\theta_2^s$ from
    $p(\theta_2|y_2)$, we can compute posterior draws for the derived
    quantity as $\delta^s=\theta_1^s-\theta_2^s$ ($s=1,\ldots,S$).
2.  $\delta^s$ are then draws from $p(\delta^s|y_1,y_2)$, and they can
    be used to illustrate the posterior $p(\delta^s|y_1,y_2)$ with
    histogram, and compute posterior mean, sd, and quantiles.

This is one reason why Monte Carlo approaches are so commonly used.

### The number of required Monte Carlo draws

This will discussed in Lecture 4 and Chapter 10. Meanwhile, e.g., 1000 draws is
sufficient.

### Bioassay

Bioassay example is is an example of very common statistical inference
task typical, for example, medicine, pharmacology, health care,
cognitive science, genomics, industrial processes etc.

The example is from Racine et al (1986) (see ref in the end of the
BDA3). Swiss company makes classification of chemicals to different
toxicity categories defined by authorities (like EU). Toxicity
classification is based on lethal dose 50% (LD50) which tells what
amount of chemical kills 50% of the subjects. Smaller the LD50 more
lethal the chemical is. The original paper mentions \"1983 Swiss poison
Regulation\" which defines following categories for chemicals orally
given to rats (mg/ml)\

   Class     LD50
  ------- -----------
     1        \<5
     2       5-50
     3      50-500
     4     500-2000
     5     2000-5000
  ------- -----------

To reduce the number of rats needed in the experiments, the company
started to use Bayesian methods. The paper mentions that in those days
use of just 20 rats to define the classification was very little. Book
gives LD50 in log(g/ml). When the result from 3.6 is transformed to
scale mg/ml, we see that the mean LD50 is about 900 and
$p(500<\text{LD50}<2000)\approx 0.99$. Thus, the tested chemical can be
classified as category 4 toxic.

Note that the chemical testing is moving away from using rats and other
animals to using, for example, human cells grown in chips, tissue models
and human blood cells. The human-cell based approaches are also more
accurate to predict the effect for humans.

$\logit$ transformation can be justified information
theoretically when binomial likelihood is used.

Code in demo 3.6 ([R](https://avehtari.github.io/BDA_R_demos/demos_ch3/demo3_6.html), [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch3/demo3_6.ipynb)) can be helpful in exercises related to Bioassay
example.

### Bayesian vs. frequentist statements in two group comparisons

When asking to compare groups, some students get confused as the
frequentist testing is quite different. The frequentist testing is often
focusing on a) differently named tests for different models and b) null
hypothesis testing. In Bayesian inference a) the same Bayes rule and
investigation of posterior is used for all models, b) null hypothesis
testing is less common. We come later to decision making given posterior
and utility/ cost function (Lecture 10.1) and more about null hypothesis
testing (Lecture 12.1). Now it is assumed you will report the posterior
(e.g. histogram), possible summaries, and report what you can infer from
that. Specifically as in this assignment the group comparisons are based
on continuous model parameter, the probability of 0 difference is 0
(later lecture 12.1 covers null hypothesis testing). Instead of forcing
dichotomous answer (yes/no) about whether there is difference, report
the whole posterior that tells also how big that difference might be.
What big means depends on the application, which brings us back to the
fact of importance of domain expertise. You are not experts on the
application examples used in the assignment, but you can think how would
you report what you have learned to a domain expert.

[Frank Harrell's recommendations how to state results in two group
comparisons](https://hbiostat.org/blog/post/bayes-freq-stmts/) are excellent.

## Chapter 4 Asymptotics and connections to non-Bayesian approaches {#ch4}

[Chapter 4](https://users.aalto.fi/~ave/BDA3.pdf#page=93) is related to the Lecture 11 *Normal approximation,
frequency properties*.

### Outline

- 4.1 Normal approximation (Laplace's method)
- 4.2 Large-sample theory
- 4.3 Counter examples
- 4.4 Frequency evaluation (not part of the course, but interesting)
- 4.5 Other statistical methods (not part of the course, but interesting)

Normal approximation is used often used as part of posterior
computation (more about this in [Chapter 13](#ch13), which is not a
part of the course).

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others. 

- sample size
- asymptotic theory
- normal approximation
- quadratic function
- Taylor series expansion
- observed information
- positive definite
- why $\log \sigma$?
- Jacobian of the transformation
- point estimates and standard errors- large-sample theory
- asymptotic normality
- consistency
- underidentified
- nonidentified
- number of parameters increasing with sample size
- aliasing
- unbounded likelihood
- improper posterior
- edge of parameter space
- tails of distribution

### R and Python demos

- 4.1: Bioassay example [R](https://avehtari.github.io/BDA_R_demos/demos_ch4/demo4_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch4/demo4_1.ipynb).

### Normal approximation

Other normal posterior approximations are discussed in [Chapter
13](ch13). For example, variational and expectation propagation methods
improve the approximation by global fitting instead of just the
curvature at the mode. The normal approximation at the mode is often
also called the Laplace method, as Laplace used it first.

Several researchers have provided partial proofs that posterior
converges towards normal distribution. In the mid 20th century Le
Cam was first to provide a strict proof.

### Observed information

When $n\rightarrow\infty$, the posterior distribution approaches
normal distribution. As the log density of the normal is a
quadratic function, the higher derivatives of the log posterior
approach zero. The curvature at the mode describes the information
only in the case if asymptotic normality. In the case of the normal
distribution, the curvature describes also the width of the
normal. Information matrix is a *precision matrix*, which is
inverse of a covariance matrix.

### Aliasing

In Finnish: valetoisto.

Aliasing is a special case of under-identifiability, where likelihood
repeats in separate points of the parameter space. That is, likelihood
will get exactly same values and has same shape although possibly
mirrored or otherwise projected. For example, the following mixture model
$$
  p(y_i|\mu_1,\mu_2,\sigma_1^2,\sigma_2^2,\lambda)=\lambda\N(\mu_1,\sigma_1^2)+(1-\lambda)\N(\mu_2,\sigma_2^2),
$$
has two normals with own means and variances. With a probability
$\lambda$ the observation comes from $\N(\mu_1,\sigma_1^2)$ and a
probability $1-\lambda$ from $\N(\mu_2,\sigma_2^2)$. This kind of
model could be used, for example, for the Newcomb's data, so that the
another normal component would model faulty measurements.  Model
does not state which of the components 1 or 2, would model good
measurements and which would model the faulty measurements. Thus it is
possible to interchange values of $(\mu_1,\mu_2)$ and
$(\sigma_1^2,\sigma_2^2)$ and replace $\lambda$ with $(1-\lambda)$ to
get the equivalent model. Posterior distribution then has two modes
which are mirror images of each other. When $n\rightarrow\infty$ modes
will get narrower, but the posterior does not converge to a single
point.

If we can integrate over the whole posterior, the aliasing is not a
problem. However aliasing makes the approximative inference more
difficult. 

### Frequency property vs. frequentist

Bayesians can evaluate frequency properties of Bayesian estimates
without being frequentist. For Bayesians the starting point is the
Bayes rule and decision theory. Bayesians care more about efficiency
than unbiasedness.  For frequentists the starting point is to find an
estimator with desired frequency properties and quite often
unbiasedness is chosen as the first restriction.

### Transformation of variables

See [BDA3 p. 21](https://users.aalto.fi/~ave/BDA3.pdf#page=31)
for the explanation how to derive densities for transformed
variables. This explains, for example, why uniform prior
$p(\log(\sigma^2))\propto 1$ for $\log(\sigma^2)$ corresponds to prior
$p(\sigma^2)=\sigma^{-2}$ for $\sigma^{2}$.

### On derivation

Here's a reminder how to integrate with respect to $g(\theta)$. For example 
$$
  \frac{d}{d\log\sigma}\sigma^{-2}=-2 \sigma^{-2}
$$
is easily solved by setting $z=\log\sigma$ to get
$$
  \frac{d}{dz}\exp(z)^{-2}=-2\exp(z)^{-3}\exp(z)=-2\exp(z)^{-2}=-2\sigma^{-2}.
$$

## Chapter 5 Hierarchical models {#ch5}

[Chapter 5](https://users.aalto.fi/~ave/BDA3.pdf#page=111) is related to the Lecture 7 *Hierarchical models and exchangeability*.

### Outline

- 5.1 Lead-in to hierarchical models
- 5.2 Exchangeability (a useful theoretical concept)
- 5.3 Bayesian analysis of hierarchical models (discusses factorized computation which can be skipped)
- 5.4 Hierarchical normal model (discusses factorized computation which can be skipped)
- 5.5 Example: parallel experiments in eight schools (useful dicussion, skip the details of computation)
- 5.6 Meta-analysis (can be skipped in this course)
- 5.7 Weakly informative priors for hierarchical variance parameters
    (more recent discussion can be found in [Prior Choice Recommendation Wiki](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations))

The hierarchical models in the chapter are simple to keep computation
simple. More advanced computational tools are presented in Chapters
[10](ch10), [11](ch11) and [12](ch12) (part of the course), and [13](ch13) (not part of the course).

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others.

- population distribution
- hyperparameter
- hierarchical model
- exchangeability
- invariant to permutations
- independent and identically distributed
- ignorance
<!-- - de Finetti's theorem -->
- partially exchangeable
- conditionally exchangeable
- conditional independence
- hyperprior
- different posterior predictive distributions
- the conditional probability formula


### R and Python demos

- 5.1: Rats example. [R](https://avehtari.github.io/BDA_R_demos/demos_ch5/demo5_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch5/demo5_1.ipynb).
- 5.2: SAT example. [R](https://avehtari.github.io/BDA_R_demos/demos_ch5/demo5_2.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch5/demo5_2.ipynb).

### Recommended exercises

Optional but recommended end of the chapter exercises in BDA3 to get a better understanding of the chapter topic:

- [5.1 and 5.2](https://users.aalto.fi/~ave/BDA3.pdf#page=144) ([model solution available for 5.3-5.5,
    5.7-5.12](http://www.stat.columbia.edu/~gelman/book/solutions3.pdf))
    
### Computation

Examples in BDA3 Sections 5.3 and 5.4 continue computation with factorization
and grid, but there is no need to go deep in to computational details as
in the assignments you will use MCMC and Stan instead.

### Exchangeability vs. independence

Exchangeability and independence are two separate concepts. Neither
necessarily implies the other. Independent identically distributed
variables/parameters are exchangeable. Exchangeability is less strict
condition than independence. Often we may assume that observations or
unobserved quantities are in fact dependent, but if we can't get
information about these dependencies we may assume those observations or
unobserved quantities as exchangeable. ``Ignorance implies
exchangeability.''

In case of exchangeable observations, we may sometimes act *as if*
observations were independent if the additional potential information
gained from the dependencies is very small. This is related to de
Finetti's theorem
([BDA3 p. 105](https://users.aalto.fi/~ave/BDA3.pdf#page=115)),
which applies formally only when
$J\rightarrow\infty$, but in practice difference may be small if $J$ is
finite but relatively large (see examples below).

- If no other information than data $y$ is available to distinguish
  $\theta_j$ from each other and parameters can not be ordered or
  grouped, we may assume symmetry between parameters in their prior
  distribution
- This symmetry can be represented with exchangeability
- Parameters $\theta_1,\ldots,\theta_J$ are exchangeable in their
  joint distribution if $p(\theta_1,\ldots,\theta_J)$ is invariant to
  permutation of indexes $(1,\ldots,J)$

Here are some examples you may consider.

Ex 5.1. Exchangeability with known model parameters: For each of
following three examples, answer: (i) Are observations $y_1$ and $y_2$
exchangeable? (ii) Are observations $y_1$ and $y_2$ independent? (iii)
Can we act *as if* the two observations are independent?

1.  A box has one black ball and one white ball. We pick a ball $y_1$ at
    random, put it back, and pick another ball $y_2$ at random.
2.  A box has one black ball and one white ball. We pick a ball $y_1$ at
    random, we do not put it back, then we pick ball $y_2$.
3.  A box has a million black balls and a million white balls. We pick a
    ball $y_1$ at random, we do not put it back, then we pick ball $y_2$
    at random.

Ex 5.2. Exchangeability with unknown model parameters: For each of
following three examples, answer: (i) Are observations $y_1$ and $y_2$
exchangeable? (ii) Are observations $y_1$ and $y_2$ independent? (iii)
Can we act *as if* the two observations are independent?

1.  A box has $n$ black and white balls but we do not know how many of
    each color. We pick a ball $y_1$ at random, put it back, and pick
    another ball $y_2$ at random.
2.  A box has $n$ black and white balls but we do not know how many of
    each color. We pick a ball $y_1$ at random, we do not put it back,
    then we pick ball $y_2$ at random.
3.  Same as (b) but we know that there are many balls of each color in
    the box.

Note that for example in opinion polls, balls i.e. humans are not put
back and there is a large but finite number of humans.

Following complements the divorce example in the book by discussing the
effect of the additional observations

- Example: divorce rate per 1000 population in 8 states of the USA in
  1981
    - without any other knowledge $y_1,\ldots,y_8$ are exchangeable
    - it is reasonable to assume a prior independence given population
      density $p(y_i|\theta)$
- Divorce rate in first seven are $5.6, 6.6, 7.8, 5.6,
      7.0, 7.2, 5.4$
    - now we have some additional information, but still changing the
      indexing does not affect the joint distribution. For example, if
      we were told that divorce rate were not for the first seven but
      last seven states, it does not change the joint distribution,
      and thus $y_1,\ldots,y_8$ are exchangeable
    - sensible assumption is a prior independence given population
      density $p(y_i|\theta)$
    - if \"true\" $\theta_0$ were known, $y_1,\ldots,y_8$ were
      independent given \"true\" $\theta_0$
    - since $\theta$ is estimated using observations, $y_i$ are a
      posterior dependent, which is obvious, e.g., from the predictive
      density $p(y_8|y_1,\ldots,y_7,M)$, i.e. e.g. if $y_1,\ldots,y_7$
      are large then probably $y_8$ is large
    - if we were told that given rates were for the last seven states,
      then $p(y_1|y_2,\ldots,y_8,M)$ would be exactly same as
      $p(y_8|y_1,\ldots,y_7,M)$ above, i.e. changing the indexing does
      not have effect since $y_1,\ldots,y_8$ are exchangeable
- Additionally we know that $y_8$ is Nevada and rates of other states
    are $5.6, 6.6, 7.8, 5.6, 7.0, 7.2, 5.4$
    - based on what we were told about Nevada, predictive density s
      $p(y_8|y_1,\ldots,y_7,M)$ should take into account that
      probability $p(y_8>\max(y_1,\ldots,y_7)|y_1,\ldots,y_7)$ should
      be large
    - if we were told that, Nevada is $y_3$ (not $y_8$ as above), then
      new predictive density $p(y_8|y_1,\ldots,y_7,M)$ would be
      different, because $y_1,\ldots,y_8$ are not anymore exchangeable

### What if observations are not exchangeable

Often observations are not fully exchangeable, but are partially or
conditionally exchangeable. Two basic cases

- If observations can be grouped, we may make hierarchical model, were
  each group has own subpart, but the group properties are unknown. If
  we assume that group properties are exchangeable we may use common
  prior for the group properties.
- If $y_i$ has additional information $x_i$, then $y_i$ are not
  exchangeable, but $(y_i,x_i)$ still are exchangeable, then we can be
  make joint model for $(y_i,x_i)$ or conditional model $(y_i|x_i)$.

Here are additional examples (Bernardo & Smith, Bayesian Theory, 1994),
which illustrate the above basic cases. Think of old fashioned thumb
pin. This kind of pin can stay flat on it's base or slanting so that the
pin head and the edge of the base touch the table. This kind of pin
represents realistic version of \"unfair\" coin.

- Let's throw pin $n$ times and mark $x_i=1$ when pin stands on it's
  base. Let's assume, that throwing conditions stay same all the time.
  Most would accept throws as exchangeable.
- Same experiment, but odd numbered throws will be made with full
  metal pin and even numbered throws with plastic coated pin. Most
  would accept exchangeability for all odd and all even throws
  separately, but not necessarily for both series combined. Thus we
  have partial exchangeability.
- Laboratory experiments $x_1,...,x_n$, are real valued measurements
  about the chemical property of some substance. If all experiments
  are from the same sample, in the same laboratory with same
  procedure, most would accept exchangeability. If experiments were
  made, for example, in different laboratories we could assume partial
  exchangeability.
- $x_1,...,x_n$ are real valued measurements about the physiological
  reactions to certain medicine. Different test persons get different
  amount of medicine. Test persons are males and females of different
  ages. If the attributes of the test persons were known, most would
  not accept results as exchangeable. In a group with certain dose,
  sex and age, the measurements could be assumed exchangeable. We
  could use grouping or if the doses and attributes are continuous we
  could use regression, that is, assume conditional independence.

### Weakly informative priors for hierarchical variance parameters

Our thinking has advanced since section 5.7 was written. Section 5.7
([BDA3 p.  128--](https://users.aalto.fi/~ave/BDA3.pdf#page=138))
recommends use of half-Cauchy as weakly informative
prior for hierarchical variance parameters. More recent recommendation
is half-normal if you have substantial information on the high end
values, or or half-$t_4$ if you there might be possibility of
surprise. Often we don't have so much prior information that we would
be able to well define the exact tail shape of the prior, but
half-normal produces usually more sensible prior predictive
distributions and is thus better justified. Half-normal leads also
usually to easier inference.
 
See the [Prior Choice Wiki](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations)
for more recent general discussion and model specific recommendations.

## Chapter 6 Model checking {#ch6}

[Chapter 6](https://users.aalto.fi/~ave/BDA3.pdf#page=151) is related to the Lecture 8 *Model checking & cross-validation*.

### Outline

- 6.1 The place of model checking in applied Bayesian statistics
- 6.2 Do the inferences from the model make sense?
- 6.3 Posterior predictive checking ($p$-values can be skipped)
- 6.4 Graphical posterior predictive checks (this can be skimmed, see
    instead the paper *Visualization in Bayesian workflow*)
- 6.5 Model checking for the educational testing example

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others.

- model checking
- sensitivity analysis
- external validation
- posterior predictive checking
- joint posterior predictive distribution
- marginal (posterior) predictive distribution
- self-consistency check
- replicated data
- $y^{\mathop{\mathrm{\mathrm{rep}}}}$, $\tilde{y}$, $\tilde{x}$
- test quantities
- discrepancy measure
- tail-area probabilities
- classical $p$-value
- posterior predictive $p$-values
- multiple comparisons
- marginal predictive checks
- cross-validation predictive distributions

### R and Python demos

- 6.1: Posterior predictive checking - speed of light. [R](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch6/demo6_1.ipynb).
- 6.2: Posterior predictive checking - sequential dependence. [R](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch6/demo6_1.ipynb).
- 6.3: Posterior predictive checking - poor test statistic. [R](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch6/demo6_1.ipynb).
- 6.4: Posterior predictive checking - marginal predictive $p$-value. [R](https://avehtari.github.io/BDA_R_demos/demos_ch6/demo6_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch6/demo6_1.ipynb).

### Recommended exercises

Optional but recommended end of the chapter exercises in BDA3 to get a better understanding of the chapter topic:

- [6.1](https://users.aalto.fi/~ave/BDA3.pdf#page=173) ([model solution available for 6.1, 6.5-6.7](http://www.stat.columbia.edu/~gelman/book/solutions3.pdf))

### Replicates vs. future observation

Predictive $\tilde{y}$ is the next not yet observed possible
observation. $y^{\mathrm{rep}}$ refers to replicating the whole
experiment (with same values of $x$) and obtaining as many replicated
observations as in the original data.

### Posterior predictive $p$-values

Section 6.3 discusses posterior predictive $p$-values, which we don't
recommend any more especially in a form of hypothesis testing.

### Prior predictive checking

Prior predictive checking using just the prior predictive distributions
is very useful tool for assessing the sensibility of the model and
priors even before observing any data or before doing the posterior
inference. See additional reading below for examples.

### Additional reading

The following article has some useful discussion and examples also about
prior and posterior predictive checking.

- Gabry, Simpson, Vehtari, Betancourt, and Gelman (2018).
  Visualization in Bayesian workflow. *Journal of the Royal
  Statistical Society Series A*, , 182(2):389-402.
  [Online](https://doi.org/10.1111/rssa.12378).
- [Video of the paper presentation](https://www.youtube.com/watch?v=E8vdXoJId8M).

And some additional useful demos

- [Graphical posterior predictive checks using the bayesplot package](http://mc-stan.org/bayesplot/articles/graphical-ppcs.html).
- [Another PPC demo](http://avehtari.github.io/BDA_R_demos/demos_rstan/ppc/poisson-ppc.html).

## Chapter 7 Evaluating, comparing, and expanding models {#ch7}

[Chapter 7](https://users.aalto.fi/~ave/BDA3.pdf#page=175) is related to the Lecture 8 *Model checking & cross-validation'' and he Lecture 9 *Model comparison and selection*.

### Outline

- 7.1 Measures of predictive accuracy
- 7.2 Information criteria and cross-validation (read instead the
    article mentioned below)
- 7.3 Model comparison based on predictive performance (read instead
    the article mentioned below)
- 7.4 Model comparison using Bayes factors (not used in the course,
    but useful to read if you have heard about Bayes factors)
- 7.5 Continuous model expansion / sensitivity analysis
- 7.6 Example (may be skipped)

Instead of Sections 7.2 and 7.3 it's better to read

- Aki Vehtari, Andrew Gelman and Jonah Gabry (2017). Practical
  Bayesian model evaluation using leave-one-out cross-validation and
  WAIC. *Statistics and Computing*, **27**(5):1413-1432,
  doi:10.1007/s11222-016-9696-4. [arXiv preprint
  arXiv:1507.04544](http://arxiv.org/abs/1507.04544).
- [LOO package glossary](https://mc-stan.org/loo/reference/loo-glossary.html)
    summarizes many important terms used in the assignments.
- [CV-FAQ](https://users.aalto.fi/~ave/CV-FAQ.html)

In Sections 7.2 and 7.3 of BDA, for historical reasons there is a
multiplier $-2$ used. After the book was published, we have concluded
that it causes too much confusion and recommend not to multiply by $-2$.
The above paper is not using $-2$ anymore.

### Extra material

The following article provides excellent discussion about "How should I
evaluate my modeling choices?" from a scientific perspective.

- Danielle J. Navarro (2019). Between the devil and the deep blue sea:
  Tensions between scientific judgment and statistical model
  selection. *Computational Brain & Behavior* **2**:28--34.
  [Online](https://doi.org/10.1007/s42113-018-0019-z).

- [Extra videos, slides, case studies, and references on model selection](https://avehtari.github.io/modelselection/)
- Sections 1 and 5 (less than 3 pages) of "[Uncertainty in Bayesian
  Leave-One-Out Cross-Validation Based Model
  Comparison](https://arxiv.org/abs/2008.10296)" clarify how to
  interpret standard error in model comparison

### The most important terms

Find all the terms and symbols listed below. When reading the chapter
and the above mentioned article, write down questions related to things
unclear for you or things you think might be unclear for others.

- predictive accuracy/fit/error
- external validation
- cross-validation
- information criteria
- overfitting
- measures of predictive accuracy
- point prediction
- scoring function
- mean squared error
- scoring rule
- logarithmic score
- log-predictive density
- out-of-sample predictive fit
- elpd, elppd, lppd
- within-sample predictive accuracy
- adjusted within-sample predictive accuracy
- AIC, DIC, WAIC (less important)
- effective number of parameters
- singular model
- leave-one-out cross-validation
- evaluating predictive error comparisons
- bias induced by model selection
- Bayes factors
- continuous model expansion
- sensitivity analysis

### Additional reading

More theoretical details can be found in

- Aki Vehtari and Janne Ojanen (2012). A survey of Bayesian predictive
  methods for model assessment, selection and comparison. In
  Statistics Surveys, 6:142-228. [Online](http://dx.doi.org/10.1214/12-SS102).

More experimental comparisons can be found in

- Juho Piironen and Aki Vehtari (2017). Comparison of Bayesian
  predictive methods for model selection. Statistics and Computing,
  27(3):711-735. [Online](http://link.springer.com/article/10.1007/s11222-016-9649-y).

### Posterior probability of the model vs. predictive performance

Gelman: "To take a historical example, I don't find it useful, from a
statistical perspective, to say that in 1850, say, our posterior
probability that Newton's laws were true was 99%, then in 1900 it was
50%, then by 1920, it was 0.01% or whatever. I'd rather say that
Newton's laws were a good fit to the available data and prior
information back in 1850, but then as more data and a clearer
understanding became available, people focused on areas of lack of fit
in order to improve the model."

Newton's laws are still sufficient for prediction in specific contexts
(non-relative speeds and differences in gravity, non-significant effects
of air resistance or other friction). See more in [the course video 1.1
Introduction to uncertainty and modeling](https://aalto.cloud.panopto.eu/Panopto/Pages/Viewer.aspx?id=d841f429-9c3d-4d24-8228-a9f400efda7b).

## Chapter 8 Modeling accounting for data collection {#ch8}

[Chapter 8](https://users.aalto.fi/~ave/BDA3.pdf#page=207) is not part of the BDA Aalto course.

In the earlier chapters it was assumed that the data collection is
ignorable. Chapter 8 explains when data collection can be ignorable and
when we need to model also the data collection. We don't have time to go
through chapter 8 in BDA course at Aalto, but it is highly recommended
that you would read it in the end or after the course. Most important
sections are 8.1, 8.5, pp. 220--222 of 8.6, and 8.8, and you can get back to
the other sections later.

### Outline

- 8.1 Bayesian inference requires a model for data collection (important)
- 8.2 Data-collection models and ignorability
- 8.3 Sample surveys
- 8.4 Designed experiments
- 8.5 Sensitivity and the role of randomization (important)
- 8.6 Observational studies (pp. 220--222 important)
- 8.7 Censoring and truncation (important)

### The most important terms

- observed data
- complete data
- missing data
- stability assumption
- data model
- inclusion model
- complete data likelihood
- observed data likelihood
- finite-population and superpopulation inference
- ignorability
- ignorable designs
- propensity score
- sample surveys
- random sampling of a finite population
- stratified sampling
- cluster sampling
- designed experiments
- complete randomization
- randomized blocks and Latin squares
- sequential designs
- randomization given covariates
- observational studies
- censoring
- truncation
- missing completely at random

## Chapter 9 Decision analysis {#ch9}

[Chapter 9](https://users.aalto.fi/~ave/BDA3.pdf#page=247) is related to the Lecture 10 *Decision analysis*.

### Outline

- 9.1 Context and basic steps (most important part)
- 9.2 Example
- 9.3 Multistage decision analysis (you may skip this example)
- 9.4 Hierarchical decision analysis (you may skip this example)
- 9.5 Personal vs. institutional decision analysis (important)

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others.

- decision analysis
- steps of Bayesian decision analysis 1--4 ([BDA3 p. 238](https://users.aalto.fi/~ave/BDA3.pdf#page=248))
- decision
- outcome
- utility function
- expected utility
- decision tree
- summarizing inference
- model selection
- individual decision problem
- institutional decision problem

### Simpler examples

The lectures have simpler examples and also discuss some challenges in
selecting utilities or costs.

### Model selection as a decision problem

[Chapter 7](ch7) discusses how model selection con be considered as a decision
problem.

## Chapter 10 Introduction to Bayesian computation {#ch10}

[Chapter 10](https://users.aalto.fi/~ave/BDA3.pdf#page=271) is related to the Lecture 4 *Monte Carlo*.

### Outline

- 10.1 Numerical integration (overview)
- 10.2 Distributional approximations (overview, more in Chapters [4](ch4)
    and [13](ch13))
- 10.3 Direct simulation and rejection sampling (overview)
- 10.4 Importance sampling (used in PSIS-LOO discussed later)
- 10.5 How many simulation draws are needed? (Important! Ex 10.1 and
    10.2)
- 10.6 Software (can be skipped)
- 10.7 Debugging (can be skipped)

Sections 10.1--10.4 give overview of different computational methods.
Some of then have been already used in the book.

Section 10.5 is very important and related to the exercises.

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others.

- unnormalized density
- target distribution
- log density
- overflow and underflow
- numerical integration
- quadrature
- simulation methods
- Monte Carlo
- stochastic methods
- deterministic methods
- distributional approximations
- crude estimation
- direct simulation
- grid sampling
- rejection sampling
- importance sampling
- importance ratios/weights

### R and Python demos

- 10.1: Rejection sampling. [R](https://avehtari.github.io/BDA_R_demos/demos_ch10/demo10_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch10/demo10_1.ipynb).
- 10.2: Importance sampling. [R](https://avehtari.github.io/BDA_R_demos/demos_ch10/demo10_2.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch10/demo10_2.ipynb).
- 10.3: Sampling-importance resampling. [R](https://avehtari.github.io/BDA_R_demos/demos_ch10/demo10_3.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch10/demo10_3.ipynb).

### Recommended exercises

Optional but recommended end of the chapter exercises in BDA3 to get a better understanding of the chapter topic:

- [10.1, 10.2](https://users.aalto.fi/~ave/BDA3.pdf#page=282) ([model solution available for 10.4](http://www.stat.columbia.edu/~gelman/book/solutions3.pdf))

### Numerical accuracy of computer arithmetic

Many models use continuous real valued parameters. Computers have finite
memory and thus the continuous values are also presented with finite
number of bits and thus with finite accuracy. Most commonly used
presentations are floating-point presentations that try to have balanced
accuracy over the range of values where it mostly matters. As the the
presentation has finite accuracy there are limitations, for example,
with IEC 60559 floating-point (double precision) arithmetic used in
current R

- the smallest positive floating-point number $x$ such that
    $1 + x \neq 1$ is $2.220446\cdot 10^{-16}$
- the smallest non-zero normalized floating-point number is
    $2.225074\cdot 10^{-308}$
- the largest normalized floating-point number
    $1.797693\cdot 10^{308}$
- the largest integer which can be represented is
    $2^{31} - 1 = 2147483647$
- see more in [R documentation: Numerical Characteristics of the Machine](https://stat.ethz.ch/R-manual/R-devel/library/base/html/zMachine.html).

- [What Is Floating-Point Arithmetic? blog post by Nick Higham](https://nhigham.com/2020/05/04/what-is-floating-point-arithmetic/) provides nice short introduction.
- [What Every Computer Scientist Should Know About Floating-Point
Arithmetic by Goldberg
(1991)](https://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html)
provides nice overview of floating-point arithmetic and how the
computations should be arranged for improved accuracy.
- [Stat 3701 Lecture Notes: Computer Arithmetic by Geyer (2020)](https://stat.umn.edu/geyer/3701/notes/arithmetic.html)
provide code examples in R illustrating the most common issues in
floating-point arithmetic including examples similar shown in the BDA
course lecture.
- [Stan User Guide Chapter 15](https://mc-stan.org/docs/2_26/stan-users-guide/floating-point-arithmetic.html)
discusses floating point arithmetic in context of Stan.

### Terms draw, draws and sample

A group of draws is a sample. A sample can consist of one draw, and
thus some people use the word sample for both single item and for the
group.  For clarity, we prefer separate words for a single item (draw)
and for the group (sample). Sample can also mean a group of
observations, and thus talking about posterior draws reduces
ambiguity.

### How many digits should be displayed

- Too many digits make reading of the results slower and give false
  impression of the accuracy.
- Show meaningful digits given the posterior uncertainty. You can
  compare posterior standard error or posterior intervals to the mean
  value. Posterior interval length can be used to determine also how
  many digits to show for the interval endpoints.
- Don't ever show digits which are just random noise. You can use Monte
  Carlo standard error estimates to check how many digits are likely
  to stay the same if the sampling would be continued.
- Example: The mean and 90% central posterior interval for temperature
  increase C$^\circ$/century (see the slides for the example) based on
  posterior draws:
    -  $2.050774$ and $[0.7472868 3.3017524]$ (too
        many digits)
    -  $2.1$ and $[0.7 3.3]$ (good compared to the
        interval length)
    -  $2$ and $[1 3]$ (sufficient accuracy in many cases)
- Example: The probability that temp increase is positive
    -  $0.9960000$ (too many digits)
    -  $1.00$ (depends on the context. $1.00$ hints it's not exactly $1$,
       but larger than $0.99$)
    - With 4000 draws MCSE $\approx 0.002$. We could report that
      probability is very likely larger than $0.99$, or sample more to
      justify reporting three digits
    - For probabilities close to 0 or 1, consider also when the model
      assumption justify certain accuracy
- When reporting many numbers in table, for aesthetics reasons, it may
  be sometimes better for some numbers to show one extra or one too
  few digits compared to the ideal.
- Often it's better to plot the whole posterior density in addition of
  any summaries, as summaries always loose some information content.
- For your reports: Don't be lazy and settle for the default number of
  digits in R or Python. Think for each reported value how many digits
  is sensible.

### Quadrature

Sometimes 'quadrature' is used to refer generically to any [numerical
integration
method](https://en.wikipedia.org/wiki/Numerical_integration)
(including Monte Carlo), sometimes it is used to refer just to
deterministic numerical integration methods.

### Rejection sampling

Rejection sampling is mostly used as a part of fast methods for
univariate sampling. For example, sampling from the normal
distribution is often made using [Ziggurat
method](https://en.wikipedia.org/wiki/Ziggurat_algorithm), which uses
a proposal distribution resembling stairs.

Rejection sampling is also commonly used for truncated distributions, in
which case all draws from the truncated part are rejected.

### Importance sampling

Popularity of importance sampling is increasing. It is used, for
example, as part of other methods as particle filters and pseudo
marginal likelihood approaches, and to improve distributional
approximations (including variational inference in machine learning).

Importance sampling is useful in importance sampling leave-one-out
cross-validation. Cross-validation is discussed in [Chapter 7](ch7) and
importance sampling leave-one-out cross-validation is discussed in the
article

- Aki Vehtari, Andrew Gelman and Jonah Gabry (2016). Practical
  Bayesian model evaluation using leave-one-out cross-validation and
  WAIC. In Statistics and Computing, 27(5):1413--1432. [arXiv preprint
  arXiv:1507.04544](http://arxiv.org/abs/1507.04544).

After the book was published, we have developed Pareto smoothed
importance sampling which is more stable than plain importance sampling
and has very useful Pareto-$k$ diagnostic to check the reliability

- Aki Vehtari, Daniel Simpson, Andrew Gelman, Yuling Yao, and Jonah
  Gabry (2021). Pareto smoothed importance sampling. [arXiv preprint
  arXiv:1507.02646](http://arxiv.org/abs/1507.02646).

### Importance resampling with or without replacement

[BDA3 p. 266](https://users.aalto.fi/~ave/BDA3.pdf#page=276)
recommends importance resampling without replacement. At the
time of writing that in 2013, we had less experience with importance
sampling and there were some reasonable papers showing reduced variance
doing resampling without replacement. We don't recommend this anymore as
Pareto smoothed importance sampling works better and is also applicable
when the resample sample size is equal to the original sample size.

### Importance sampling effective sample size

BDA3 1st (2013) and 2nd (2014) printing have an error for
$\tilde{w}(\theta^s)$ used in the effective sample size equation 10.4.
The normalized weights equation should not have the multiplier S (the
normalized weights should sum to one). 
The effective sample size estimate mentioned in the book is generic
approximation, and more accurate effective sample size estimate would
take into account also the functional. For example, importance sampling
effective sample size can be different when estimating
$\E[\theta]$ or $\E[\theta]^2$. If you
are interested see more details, for example, [the Pareto smoothed importance
sampling paper](https://arxiv.org/abs/1507.02646).

The derivation for the effective sample size and Monte Carlo standard
error (MCSE) for importance sampling can be found, for example, in
[Chapter 9 of *Monte Carlo theory, methods and examples* by Art
B. Owen](https://statweb.stanford.edu/~owen/mc/).

### Buffon's needles

Buffon's needle is considered to be the first Monte Carlo style
approach presented in 1777. It's not known whether Buffon actually did
the experiment in addition of describing the approach for estimating
the value of $\pi$. Check out [a nice computer simulation of Buffon's
needle dropping method](https://mste.illinois.edu/activity/buffon/).

## Chapter 11 Basics of Markov chain simulation {#ch11}

[Chapter 11](https://users.aalto.fi/~ave/BDA3.pdf#page=285) is related to the Lecture 5 *Markov chain Monte Carlo*.

### Outline

- Markov chain simulation: before Section 11.1, pages 275-276
- 11.1 Gibbs sampler (an example of simple MCMC method)
- 11.2 Metropolis and Metropolis-Hastings (an example of simple MCMC
    method)
- 11.3 Using Gibbs and Metropolis as building blocks (can be skipped)
- 11.4 Inference and assessing convergence (important)
- 11.5 Effective number of simulation draws (important)
- 11.6 Example: hierarchical normal model (skip this)

### The most important terms

Find all the terms and symbols listed below. When reading the chapter,
write down questions related to things unclear for you or things you
think might be unclear for others.

- Markov chain
- Markov chain Monte Carlo
- random walk
- starting point
- transition distribution
- jumping / proposal distribution
- to converge, convergence, assessing convergence
- stationary distribution, stationarity
- effective number of simulations
- Gibbs sampler
- Metropolis sampling / algorithm
- Metropolis-Hastings algorithm
- acceptance / rejection rule
- acceptance / rejection rate
- within-sequence correlation, serial correlation
- warm-up / burn-in
- to thin, thinned
- overdispersed starting points
- mixing
- to diagnose convergence
- between- and within-sequence variances
- potential scale reduction, $\widehat{R}$
- the variance of the average of a correlated sequence
- autocorrelation
- variogram
- $n_{\mathrm{eff}}$ (we now prefer ESS / $S_{\mathrm{eff}}$, which is used also in slides)

### R and Python

- 11.1: Gibbs sampling. [R](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_1.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch11/demo11_1.ipynb).
- 11.2: Metropolis sampling. [R](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_2.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch11/demo11_2.ipynb).
- 11.3: Convergence of Markov chain. [R](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_3.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch11/demo11_3.ipynb).
- 11.4: potential scale reduction $\widehat{R}$. [R](https://avehtari.github.io/BDA_R_demos/demos_ch11/demo11_4.html). [Python](https://github.com/avehtari/BDA_py_demos/blob/master/demos_ch11/demo11_4.ipynb).

### Recommended exercises

Optional but recommended end of the chapter exercises in BDA3 to get a better understanding of the chapter topic:

- [11.1](https://users.aalto.fi/~ave/BDA3.pdf#page=301) ([model solution available for 11.1](http://www.stat.columbia.edu/~gelman/book/solutions3.pdf))

### Basics of Markov chains

[Slides by J. Virtamo for the course S-38.143 Queuing
Theory](http://www.netlab.tkk.fi/opetus/s38143/luennot/english.shtml)
([Finnish
version](http://www.netlab.hut.fi/opetus/s38143/luennot/index.shtml))
have a nice review of the fundamental terms. See specially the slides
for the lecture 4. To prove that Metropolis algorithm works, it is
sufficient to show that chain is irreducible, aperiodic and not
transient.

### Animations

- [Nice animations of some MCMC algorithms with discussion](http://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/)
- [And just the animations with more options to experiment](https://chi-feng.github.io/mcmc-demo/)

### Metropolis algorithm

There is a lot of freedom in selection of proposal distribution in
Metropolis algorithm. There are some restrictions, but we don't go to
the mathematical details in this course.

Don't confuse rejection in the rejection sampling and in Metropolis
algorithm. In the rejection sampling, the rejected samples are thrown
away. In Metropolis algorithm the rejected proposals are thrown away,
but time moves on and the previous sample $x_t$ is also the sample
$x_{t+1}$.

When rejecting a proposal, the previous sample is repeated in the chain,
they have to be included and they are valid samples from the
distribution. For basic Metropolis, it can be shown that optimal
rejection rate is 55%--77%, so that on even the optimal case quite many
of the samples are repeated samples. However, high number of rejections
is acceptable as then the accepted proposals are on average further away
from the previous point. It is better to jump further away 23%--45% of
time than more often to jump really close. Methods for estimating the
effective sample size are useful for measuring how effective a given
chain is.

### Transition distribution vs. proposal distribution

Transition distribution is a property of Markov chain. In Metropolis
algorithm the transition distribution is a mixture of a proposal
distribution and a point mass in the current point. The book uses also
term jumping distribution to refer to proposal distribution.

### Convergence

Theoretical convergence in an infinite time is different than practical
convergence in a finite time. There is no exact moment when chain has
converged and thus it is not possible to detect when the chain has
converged (except for rare *perfect sampling* methods not discussed in
BDA3). The convergence diagnostics can help to find out if the chain is
unlikely to be representative of the target distribution. Furthermore,
even if would be able to start from a independent sample from the
posterior so that chain starts from the convergence, the mixing can be
so slow that we may require very large number of samples before the
samples are representative of the target distribution.

If starting point is selected at or near the mode, less time is needed
to reach the area of essential mass, but still the samples in the
beginning of the chain are not representative of the true distribution
unless the starting point was somehow samples directly from the target
distribution.

### $\widehat{R}$, effective sample size (ESS, previously $n_\mathrm{eff}$)

There are many versions of $\widehat{R}$ and effective sample size.
Beware that some software packages compute $\widehat{R}$ using old
inferior approaches.

The $\widehat{R}$ and the approach to estimate effective sample size
were updated in BDA3, and slightly updated version of this is described
in Stan 2.18+ user guide. Since then we have developed even better
$\widehat{R}$, ESS (effective sample size with change from
$n_\mathrm{eff}$ to ESS is due to improved consistency in the notation)
in

- Aki Vehtari, Andrew Gelman, Daniel Simpson, Bob Carpenter, and
  Paul-Christian Bürkner (2020). Rank-normalization, folding, and
  localization: An improved $\widehat{R}$ for assessing convergence
  of MCMC. *Bayesian analysis*, [Online](https://projecteuclid.org/euclid.ba/1593828229).

New $\widehat{R}$, ESS, and Monte Carlo error estimates are available in
RStan `monitor` function in R, in `posterior` package in R, and in ArviZ
package in Python.

Due to randomness in chains, $\widehat{R}$ may get values slightly below
1.

[Brief Guide to Stan's Warnings](https://mc-stan.org/misc/warnings.html)
provides summary of available convergence diagnostics in Stan and how to
interpret them.

Sometimes people write *the number of effective samples'' which is wrong
(it is possible that notation $n_\mathrm{eff}$ is partially to blame for
this misconception). All the posterior draws in autocorrelated Markov
chain are effective, but their efficiency for estimating an expectation
depends on the autocorrelation. The effective sample size is not
property of individual draws, but joint property of all draws in a
sample. Effective sample size also depends on the functional and the
effective sample size for a given dependent sample is often different
when estimating, for example, $\E[\theta]$ or
$\E[\theta^2]$. 

## Chapter 12 Computationally efficient Markov chain simulation {#ch12}

[Chapter 12](https://users.aalto.fi/~ave/BDA3.pdf#page=303) is related to the Lecture 6 *Stan, HMC, PPL*.

### Outline

- 12.1 Efficient Gibbs samplers (not part of the course)
- 12.2 Efficient Metropolis jump rules (not part of the course)
- 12.3 Further extensions to Gibbs and Metropolis (not part of the
    course)
- 12.4 Hamiltonian Monte Carlo (used in Stan)
- 12.5 Hamiltonian dynamics for a simple hierarchical model (read
    through)
- 12.6 Stan: developing a computing environment (read through)

For the BDA course, there are only 8 pages to read (Sections
12.4--12.6) what is inside Stan.

### R and Python demos
g
- 12.1: The No-U-Turn Sampler (NUTS) / Dynamic Hamiltonian Monte Carlo. [R](https://avehtari.github.io/BDA_R_demos/demos_ch12/demo12_1.html).
- [CmdStanR Demos](http://avehtari.github.io/BDA_R_demos/demos_rstan/cmdstanr_demo.html)
- [PyStan demos](https://github.com/avehtari/BDA_py_demos/blob/master/demos_pystan/pystan_demo.ipynb)

### MCMC animations

These don't include the specific version of dynamic HMC in Stan, but are
useful illustrations anyway.

- [The No-U-Turn Sampler (NUTS) / Dynamic Hamiltonian Monte Carlo R demo](https://avehtari.github.io/BDA_R_demos/demos_ch12/demo12_1.html)
- [Markov Chains: Why Walk When You Can Flow?](http://elevanth.org/blog/2017/11/28/build-a-better-markov-chain/)
- [MCMC animation site by Chi Feng](https://chi-feng.github.io/mcmc-demo/)

### Hamiltonian Monte Carlo

An excellent review of static HMC (the number of steps in dynamic
simulation are not adaptively selected) is

- Radford Neal (2011). MCMC using Hamiltonian dynamics. In Brooks et
    al (ed), *Handbook of Markov Chain Monte Carlo*, Chapman & Hall /
    CRC Press. [Preprint](https://arxiv.org/pdf/1206.1901.pdf).

Stan uses a variant of dynamic Hamiltonian Monte Carlo (using adaptive
number of steps in the dynamic simulation), which has been further
developed since BDA3 was published. The first dynamic HMC variant was
NUTS

- Matthew D. Hoffman, Andrew Gelman (2014). The No-U-Turn Sampler:
  Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. *JMLR*,
  15:1593--1623. [Online](http://jmlr.org/papers/v15/hoffman14a.html).

The No-U-Turn Sampler gave the name NUTS which you can see often
associated with Stan, but the current variant implemented in Stan has
some further developments described (mostly) in

- Michael Betancourt (2018). A Conceptual Introduction to Hamiltonian
  Monte Carlo. [arXiv preprint
  arXiv:1701.02434](https://arxiv.org/abs/1701.02434).

### Divergences and BFMI

- Divergence diagnostic checks whether the discretized dynamic
  simulation has problems due to fast varying density. See more in [a Stan
  case study](http://mc-stan.org/users/documentation/case-studies/divergences_and_bias.html).
- [Brief Guide to Stan's Warnings](https://mc-stan.org/misc/warnings.html)
  provides summary of available convergence diagnostics in Stan,d how
  to interpret them, and suggestions for solving sampling problems.

### Further information about Stan

- [Stan web page](http://mc-stan.org/)
- [Stan Documentation](http://mc-stan.org/documentation/)
- I recommend to start with these
    - Bob Carpenter, Andrew Gelman, Matt Hoffman, Daniel Lee, Ben
      Goodrich, Michael Betancourt, Marcus A. Brubaker, Jiqiang
      Guo, Peter Li, and Allen Riddell (2015) In press for Journal
      of Statistical Software. Stan: A Probabilistic Programming
      Language. [Preprint](http://www.stat.columbia.edu/~gelman/research/published/stan-paper-revision-feb2015.pdf).
    - Andrew Gelman, Daniel Lee, and Jiqiang Guo (2015) Stan: A
      probabilistic programming language for Bayesian inference
      and optimization. Journal of Educational and
      Behavior Science. [Preprint](http://www.stat.columbia.edu/~gelman/research/published/stan_jebs_2.pdf).
    - [Video of Basics of Bayesian inference and Stan tutorial by Jonah Gabry &
        Lauren Kennedy](https://www.youtube.com/playlist?list=PLuwyh42iHquU4hUBQs20hkBsKSMrp6H0J)
- More complete information (no need to read the from the beginning to end, but use when needed)
    - [Stan User’s Guide](https://mc-stan.org/docs/stan-users-guide/index.html)
    - [Stan Reference Manual](https://mc-stan.org/docs/stan-users-guide/index.html)
    - [Stan Functions Reference](https://mc-stan.org/docs/functions-reference/index.html)
    - [CmdStanR documentation](https://mc-stan.org/cmdstanr/)

### Compiler and transpiler

This is a minor comment on the terminology. As a shorthand it's common
to see mentioned just Stan compiler, but sometimes the transpiler term
is also mentioned as in the slides for this part.

[A Wikipedia
article](https://en.wikipedia.org/wiki/Source-to-source_compiler)
states: *A source-to-source translator, source-to-source compiler (S2S
compiler), transcompiler, or transpiler is a type of translator
that takes the source code of a program written in a programming
language as its input and produces an equivalent source code in the
same or a different programming language. A source-to-source
translator converts between programming languages that operate at
approximately the same level of abstraction, while a traditional
compiler translates from a higher level programming language to a
lower level programming language.*

So it is more accurate to say that the Stan model code is first
transpiled to a C++ code, and then that C++ code is compiled to machine
code to create an executable program. Cool thing about [the new stanc3
transpiler](https://github.com/stan-dev/stanc3) is that it can create
also, for example, LLVM IR or TensorFlow code.

Using transpiler and compiler allows to develop Stan language to be good
for writing models, but get the benefit of speed and external libraries
of C++, TensorFlow, and whatever comes in the future.

## Chapter 13 Modal and distributional approximations

[Chapter 13](https://users.aalto.fi/~ave/BDA3.pdf#page=321) is not part of the BDA Aalto course.

[Chapter 4](ch4) presented normal distribution approximation at the
mode (aka Laplace approximation). Chapter 13 discusses more about
distributional approximations.

### Outline

- Finding posterior modes
    - Newton's method is very fast if the distribution is close to
      normal and the computation of the second derivatives is fast
    - Stan uses limited-memory Broyden-Fletcher-Goldfarb-Shannon
      (L-BFGS) which is a quasi-Newton method which needs only the
      first derivatives (provided by Stan autodiff). L-BFGS is known
      for good performance for wide variety of functions.
- Boundary-avoiding priors for modal summaries
    - Although full integration is preferred, sometimes optimization
      of some parameters may be sufficient and faster, and then
      boundary-avoiding priors maybe useful.
- Normal and related mixture approximations
    - Discusses how the normal approximation can be used to
      approximate integrals of a a smooth function times the
      posterior.
    - Discusses mixture and $t$ approximations.
- Finding marginal posterior modes using EM
    - Expectation maximization is less important in the time of
      efficient probabilistic programming frameworks, but can be
      sometimes useful for extra efficiency.
- Conditional and marginal posterior approximations
    - Even in the time of efficient probabilistic programming, the
      methods discussed in this section can produce very big speedups
      for a big set of commonly used models. The methods discussed are
      important part of popular INLA software and are coming also to
      Stan to speedup latent Gaussian variable models.
- Example: hierarchical normal model
- Variational inference
    - Variational inference (VI) is very popular in machine learning,
      and this section presents it in terms of BDA. Auto-diff
      variational inference in Stan was developed after BDA3 was
      published.
- Expectation propagation
    - Practical efficient computation for expectation propagation (EP)
      is applicable for more limited set of models than post-BDA3
      black-box VI, but for those models EP provides better posterior
      approximation. Variants of EP can be used for parallelization of
      any Bayesian computation for hierarchical models.
- Other approximations
    - Just briefly mentions of INLA (in 13.5),
      CCD (deterministic adaptive quadrature approach) and ABC
      (inference when you can only sample from the generative model).
- Unknown normalizing factors
    - Often the normalizing factor is not needed, but it can be
      estimated using importance, bridge or path sampling.

## Chapter 14 Introduction to regression models {#ch14}

[Chapter 14](https://users.aalto.fi/~ave/BDA3.pdf#page=363) is not part of the BDA Aalto course.

### Outline

- Conditional modeling
    - formal justification of conditional modeling
    - if joint model factorizes
      $p(y,x|\theta,\phi)=p(y|x,\theta)}p(x|\phi)$\
      we can model just $p(y|x,\theta)}$
- Bayesian analysis of classical regression
    - uninformative prior on $\beta$ and $\sigma^2$
    - connection to multivariate normal (cf. Chapter 3) is useful to
      understand as it then reveals what would be the conjugate prior
    - closed form posterior and posterior predictive distribution
    - these properties are sometimes useful and thus good to know, but
      with probabilistic programming less often needed
- Regression for causal inference: incumbency and voting
    - Modeling example with bit of discussion on causal inference
      ([see more in Regression and Other Stories (ROS)](https://avehtari.github.io/ROS-Examples/) Chs. 18-21)
- Goals of regression analysis
    - discussion of what we can do with regression analysis (see more
      in [ROS](https://avehtari.github.io/ROS-Examples/))
- Assembling the matrix of explanatory variables
    - transformations, nonlinear relations, indicator variables,
      interactions (see more in [ROS](https://avehtari.github.io/ROS-Examples/))
- Regularization and dimension reduction
    - a bit outdated and short (Bayesian Lasso is not a good idea),
      see more in Lecture 9.3.
- Unequal variances and correlations
    - useful concept, but computation is easier with probabilistic
      programming frameworks
- Including numerical prior information
    - useful conceptually, but easy computation with probabilistic
      programming frameworks makes it easier to define prior
      information as the prior doesn't need to be conjugate
    - see more about priors in [Prior Choice Recommendations Wiki](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations)

## Chapter 15 Hierarchical linear models

[Chapter 15](https://users.aalto.fi/~ave/BDA3.pdf#page=391) is not part of the BDA Aalto course.

Chapter 15 combines hierarchical models from [Chapter 5](ch5) and linear models
from [Chapter 14](ch14). The chapter discusses some computational issues, but
probabilistic programming frameworks make computation for hierarchical
linear models easy.

### Outline

- Regression coefficients exchangeable in batches
    - exchangeability of parameters
    - the discussion of fixed-, random- and mixed-effects models is
        incomplete
        - we don't recommend using these terms, but they are so
            popular that it's useful to know them
        - a relevant comment is *The terms 'fixed' and 'random' come
            from the non-Bayesian statistical tradition and are somewhat
            confusing in a Bayesian context where all unknown parameters
            are treated as 'random' or, equivalently, as having fixed
            but unknown values.*
        - often fixed effects correspond to population level
            coefficients, random effects correspond to group or
            individual level coefficients, and mixed model has both
            ------------------------------- -----------------------------------------
            `y \sim 1 + x`                  fixed / population effect; pooled model
            `y \sim 1 + (0 + x | g) `       random / group effects
            `y \sim 1 + x + (1 + x | g) `   mixed effects; hierarchical model
            ------------------------------- -----------------------------------------

- Example: forecasting U.S. presidential elections
    - illustrative example
- Interpreting a normal prior distribution as extra data
    - includes very useful interpretation of hierarchical linear model
      as a single linear model with certain design matrix
- Varying intercepts and slopes
    - extends from hierarchical model for scalar parameter to joint
      hierarchical model for several parameters
- Computation: batching and transformation
    - Gibbs sampling part is mostly outdated
    - transformations for HMC is useful if you write your own models,
      but the section is quite short and you can get more information
      from Stan user guide 21.7 Reparameterization and [Divergences case study](https://mc-stan.org/users/documentation/case-studies/divergences_and_bias.html)
- Analysis of variance and the batching of coefficients
    - ANOVA as Bayesian hierarchical linear model
    - rstanarm and brms packages make it easy to make ANOVA
- Hierarchical models for batches of variance components
    - more variance components

## Chapter 16 Generalized linear models

[Chapter 16](https://users.aalto.fi/~ave/BDA3.pdf#page=415) is not part of the BDA Aalto course.

Chapter 16 extends linear models to have non-normal observation models.
Model in Bioassay example in [Chapter 3](ch3) is also generalized linear model.
Chapter reviews the basics and discusses some computational issues, but
probabilistic programming frameworks make computation for generalized
linear models easy (especially with rstanarm and brms). See [ROS](https://avehtari.github.io/ROS-Examples/)) for discussion of generalized
linear models from the modeling perspective more thoroughly.

### Outline

- Parts of generalized linear model (GLM):
    - The linear predictor $\eta = X\beta$
    - The link function $g(\cdot)$ and $\mu = g^{-1}(\eta)$
    - Outcome distribution model with location parameter $\mu$
        - the distribution can also depend on dispersion parameter $\phi$
        - originally just exponential family distributions (e.g.
          Poisson, binomial, negative-binomial), which all have
          natural location-dispersion parameterization
        - after MCMC made computation easy, GLM can refer to models
          where outcome distribution is not part of exponential family
          and dispersion parameter may have its own latent linear
          predictor
- Standard generalized linear model likelihoods
    - section title says "likelihoods", but it would be better to say
      "observation models"
    - continuous data: normal, gamma, Weibull mentioned, but common
      are also Student's $t$, log-normal, log-logistic, and various
      extreme value distributions like generalized Pareto distribution
    - binomial (Bernoulli as a special case) for binary and count data
      with upper limit
        - Bioassay model uses binomial observation model
    - Poisson for count data with no upper limit
        - Poisson is useful approximation of Binomial when the
          observed counts are much smaller than the upper limit
- Working with generalized linear models
    - bit of this and that information on how think about GLMs (see
      [ROS](https://avehtari.github.io/ROS-Examples/) for more)
    - normal approximation to the likelihood is good for thinking how
      much information non-normal observations provide, can be useful
      for someone thinking about computation, but easy computation
      with probabilistic programming frameworks means not everyone
      needs this
- Weakly informative priors for logistic regression
    - an excellent section although the recommendation on using Cauchy
       has changed (see [Prior Choice Recommendations Wiki](https://github.com/stan-dev/stan/wiki/Prior-Choice-Recommendations))
    - the problem of separation is useful to understand
    - computation part is outdated as probabilistic programming
      frameworks make the computation easy
- Overdispersed Poisson regression for police stops
    - an example
- State-level opinions from national polls
    - another example
- Models for multivariate and multinomial responses
    - extension to multivariate responses
    - polychotomous data with multivariate binomial or Poisson
    - models for ordered categories
- Loglinear models for multivariate discrete data
    - multinomial or Poisson as loglinear models

## Chapter 17 Models for robust inference

[Chapter 17](https://users.aalto.fi/~ave/BDA3.pdf#page=445) is not part of the BDA Aalto course.

Chapter 17 discusses over-dispersed observation models. The discussion
is useful beyond generalized linear models. The computation is outdated.
See [ROS](https://avehtari.github.io/ROS-Examples/) for
more examples.

### Outline

- Aspects of robustness
    - overdispersed models are often connected to robustness of
      inferences to outliers, but the observed data can be
      overdispersed without any observation being outlier
    - outlier is sensible only in the context of the model, being
      something not well modeled or something requiring extra model
      component
    - switching to generic overdispersed model can help to recognize
      problem in the non-robust model (sensitivity analysis), but it
      can also throw away useful information in the "outliers" and it
      would be useful to think what is the generative mechanism for
      observations which are not like others
- Overdispersed versions of standard models:
    -  normal      $\rightarrow$  $t$-distribution
    -  Poisson     $\rightarrow$  negative-binomial
    -  binomial    $\rightarrow$  beta-binomial
    -  probit      $\rightarrow$  logistic / robit
- Posterior inference and computation
    - computation part is outdated as probabilistic programming
      frameworks and MCMC make the computation easy
    - posterior is more likely to be multimodal
- Robust inference for the eight schools
    - eight schools example is too small too see much difference
- Robust regression using t-distributed errors
    - computation part is outdated as probabilistic programming
      frameworks and MCMC make the computation easy
    - posterior is more likely to be multimodal

## Chapter 18 Models for missing data

Chapter 18 is not part of the BDA Aalto course.

Chapter 18 extends the data collection modeling from Chapter 8. See
[ROS](https://avehtari.github.io/ROS-Examples/) for more
examples.

### Outline

- Notation
    - Missing completely at random (MCAR)
      missingness does not depend on missing values or other observed
      values (including covariates)
    - Missing at random (MAR)\
      missingness does not depend on missing values but may depend on
      other observed values (including covariates)
    - Missing not at random (MNAR)\
      missingness depends on missing values
- Multiple imputation
    - make a model predicting missing data
    - sample repeatedly from the missing data model to generate
        multiple imputed data sets
    - make usual inference for each imputed data set
    - combine results
    - discussion of computation is partially outdated
- Missing data in the multivariate normal and $t$ models
    - a special continuous data case computation, which can still be
       useful as fast starting point
- Example: multiple imputation for a series of polls
    - an example
- Missing values with counted data
    - discussion of computation for count data (ie. computation in 18.3
       is not applicable)
- Example: an opinion poll in Slovenia
    - another example