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interactive.qmd
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interactive.qmd
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---
title: "Interactive"
toc: true
---
This page contains interactive widgets for the book.
#### Chapter 1 - The Bayesics
| | | | |
|-------------------|------------------|------------------|------------------|
| <font color="#6C8EBF">The Bernoulli distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/bernoulli-distribution) | | |
| Maximum likelihood iid Bernoulli data | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/maximum-likelihood-bernoulli-data) | | |
| Bayes' theorem for events | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/bayes-theorem-for-events) | | |
: {.striped}
#### Chapter 2 - One-parameter models
| | | | |
|-------------------|------------------|------------------|------------------|
| <font color="#6C8EBF">Beta distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](#0) | | |
| Bayesian inference for iid Bernoulli data | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/beta-distribution) | | |
| <font color="#6C8EBF">Normal distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/normal-gaussian-distribution) | | |
| Bayesian inference for Gaussian iid data with known variance | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/bayes-iid-gaussian-known-var) | | |
| <font color="#6C8EBF">Poisson distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/poisson-distribution) | | |
| <font color="#6C8EBF">Gamma distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/gamma-distribution) | | |
| Bayesian inference for iid Poisson counts | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/bayesian-inference-for-iid-poisson-counts) | | |
| <font color="#6C8EBF">Exponential distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/exponential-distribution) | | |
| Bayesian inference for Exponential iid data | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/bayesian-inference-for-exponential-iid-data) | | |
: {.striped}
#### Chapter 3 - Multi-parameter models
| | | | |
|-------------------|------------------|------------------|------------------|
| <font color="#6C8EBF">Multinomial distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](#0) | | |
| <font color="#6C8EBF">Dirichlet distribution</font> | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/dirichlet-distribution) | | |
| **Bayesian inference for multinomial data** | [{alt="Julia" fig-alt="The Bernoulli distribution" fig-align="left" width="25"}](https://observablehq.com/@mattiasvillani/multinomial-dirichlet) | | |
: {.striped}
#### Chapter 4 - Priors
#### Chapter 5 - Regression
#### Chapter 6 - Prediction and Decision making
#### Chapter 7 - Normal posterior approximation
#### Chapter 8 - Classification
#### Chapter 9 - Gibbs sampling
#### Chapter 10 - Markov Chain Monte Carlo simulation
#### Chapter 11 - Variational inference
#### Chapter 12 - Regularization
#### Chapter 13 - Mixture models and Bayesian nonparametrics
#### Chapter 14 - Model comparison and variable selection
#### Chapter 15 - Gaussian processes
#### Chapter 16 - Interaction models
#### Chapter 17 - Dynamic models and sequential inference
| | | | |
|-------------------|------------------|------------------|------------------|
| Kalman filter and parameter estimation | [{alt="Julia" fig-alt="Gibbs sampling from a multivariate normal - Julia" fig-align="left" width="25"}](https://gist.github.com/mattiasvillani/4a461587c037d257d7099b7675aa0ddf) | | |
: {.striped}