Interactive
This page contains interactive widgets for the book.
Chapter 1 - The Bayesics
| The Bernoulli distribution |  |
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| Maximum likelihood iid Bernoulli data | |
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| Bayes’ theorem for events | |
Chapter 2 - One-parameter models
| Beta distribution | |
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| Bayesian inference for iid Bernoulli data | |
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| Normal distribution | |
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| Bayesian inference for Gaussian iid data with known variance | |
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| Poisson distribution | |
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| Gamma distribution | |
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| Bayesian inference for iid Poisson counts | |
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| Exponential distribution | |
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| Bayesian inference for Exponential iid data | |
Chapter 3 - Multi-parameter models
| Multinomial distribution | |
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| Dirichlet distribution | |
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| Bayesian inference for multinomial data | |
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 | |