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notebooks.qmd
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notebooks.qmd
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---
title: "Notebooks"
---
This page contains a set of notebooks in Julia, R and Python for some of the data analyses presented in the book.
#### Chapter 1 - The Bayesics
#### Chapter 2 - One-parameter models
+--------------------------------------------------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------+
| Bernoulli model for spam data | [{fig-align="left" width="20"}](notebooks/SpamBern/BernBeta.jl) | [{fig-align="left" width="20" height="16"}](notebooks/SpamBern/SpamBernR.ipynb) | |
+--------------------------------------------------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------+
| Normal model for internet download speed data | | [{alt="Julia" fig-align="left" width="20" height="16"}](notebooks/DownloadSpeedNormal/DownloadSpeedNormalR.ipynb) | |
+--------------------------------------------------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------+
| Poisson for number of eBay bidders | | [{alt="Julia" fig-align="left" width="20" height="16"}](notebooks/ebayPoissonOneParam/eBayPoissonR.ipynb) | [{alt="Julia" fig-align="left" width="20"}](notebooks/ebayPoissonOneParam/eBayPoissonPython.ipynb) |
+--------------------------------------------------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------------------+
: {.striped}
#### Chapter 3 - Multi-parameter models
+--------------------------------------------------------+---------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------+---+
| Multinomial model for survey data | [{alt="figs/julialogo.svg" width="20"}](notebooks/SurveyMultinomial/multinom.jl) | [{alt="figs/Rlogo.png" width="20" height="16"}](notebooks/SurveyMultinomial/multinomial.Rmd) | |
+--------------------------------------------------------+---------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------+---+
: {.striped}
####
#### Chapter 4 - Priors
#### Chapter 5 - Regression
#### Chapter 6 - Prediction and Decision making
#### Chapter 7 - Normal posterior approximation
#### Chapter 8 - Classification
+----------------------------------------------+-------+-------------------------------------------------------------------------------------------------------------------+-------+
| Logistic regression for spam data | | [{alt="figs/Rlogo.png" width="20" height="16"}](notebooks/SpamLogisticReg/SpamLogisticReg.Rmd) | |
+----------------------------------------------+-------+-------------------------------------------------------------------------------------------------------------------+-------+
: {.striped}
#### 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
+------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+---------+
| Filtering and smoothing of the Nile river data | [{alt="Julia" fig-alt="Gibbs sampling from a multivariate normal - Julia" fig-align="left" width="20"}](notebooks/KalmanFilteringSmoothing/R_KalmanFilteringSmoothing.ipynb) | | |
+------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+---------+
: {.striped}