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 |  |
 |
|
| Normal model for internet download speed data | |
||
| Poisson for number of eBay bidders | |
 |
Chapter 3 - Multi-parameter models
| Multinomial model for survey data | |
|
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 | |
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 | |