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 |  |
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| Normal model for internet download speed data | |
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| Poisson for number of eBay bidders | |
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Chapter 3 - Multi-parameter models
| Multinomial model for survey data | |
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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 | |
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| Logistic regression for Titanic data | |
Chapter 9 - Gibbs sampling
Chapter 10 - Markov Chain Monte Carlo simulation
Chapter 11 - Variational inference
Chapter 12 - Regularization
| Polynomial regression fossil data |  |
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 | |