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code.qmd
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code.qmd
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---
title: "Code"
toc: true
---
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 | [{alt="Julia" fig-alt="Gibbs sampling from a multivariate normal - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/e10f75403abbd992e28d6427777fbb03) | | |
| Gibbs sampling - mixture of normals | [{alt="Julia" fig-alt="Gibbs sampling - mixture of normals - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/92e82ca32b6c5334983eef3fe95b8045) | [{alt="R" fig-alt="Gibbs sampling - mixture of normals - R" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/27187865da917ba7fe29170c03a367c6) | |
| Gibbs sampling - mixture of Poissons | [{alt="Julia" fig-alt="Gibbs sampling - mixture of Poissons - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/d71fbadc3a431109e82f1f843a09fa7b) | | |
| Gibbs sampling - probit regression | [{alt="Julia" fig-alt="Gibbs sampling - probit regression - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/09eeda1231e85e59ea3b22d22be1e181) | | |
| Gibbs sampling - logistic regression | [{alt="Julia" fig-alt="Gibbs sampling - logistic regression - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/05df2e1657b336e049de683b394a6577) | | |
| Gibbs sampling - autoregressive processes | [{alt="Julia" fig-alt="Gibbs sampling - AR processes - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/aed206ee2f6166b75b4361628615f7a1) | | |
: {.striped }
#### Chapter 10 - Markov Chain Monte Carlo simulation
#### Chapter 11 - Variational inference
#### Chapter 12 - Regularization
| | | | |
|-------------------|------------------|------------------|------------------|
| Gibbs sampling - linear regression with L2-regularization | [{alt="Julia" fig-alt="Gibbs sampling from a multivariate normal - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/2edd03a1f7b98f41ac46035077854965) | [{alt="R" fig-alt="Gibbs sampling from a multivariate normal - R" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/b1a5afc8417a3cbd40374050f64cc8fe) | |
: {.striped }
#### 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 | [{alt="Julia" fig-alt="Gibbs sampling from a multivariate normal - Julia" fig-align="left" width="20"}](https://gist.github.com/mattiasvillani/4a461587c037d257d7099b7675aa0ddf) | | |
: {.striped }