Code
This page contains code snippets for algorithms in the book, sometimes in multiple languages. Click on the language icons to view and download the code.
Chapter 1 - The Bayesics
Chapter 2 - One-parameter models
Chapter 3 - Multi-parameter models
Chapter 4 - Priors
Chapter 5 - Regression
Chapter 6 - Prediction and Decision making
Chapter 7 - Normal posterior approximation
Chapter 8 - Classification
Chapter 9 - Gibbs sampling
| Gibbs sampling - multivariate normal |  |
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| Gibbs sampling - mixture of normals | |
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| Gibbs sampling - mixture of Poissons | |
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| Gibbs sampling - probit regression | |
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| Gibbs sampling - logistic regression | |
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| Gibbs sampling - autoregressive processes | |
Chapter 10 - Markov Chain Monte Carlo simulation
Chapter 11 - Variational inference
Chapter 12 - Regularization
| Gibbs sampling - linear regression with L2-regularization | |
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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 | |