The Sampling Book Project

This is a series of tutorials on sampling algorithms built with the Blackjax library. We aim to help beginners understand the mechanisms behind the samplers they commonly use, and provide comparisons between algorithms for the more advanced users.

Algorithms

• Contour stochastic gradient Langevin dynamics
• Cyclical SGLD
• Pathfinder
• Periodic Orbital MCMC
• Use Tempered SMC to Improve Exploration of MCMC Methods.
• Tuning inner kernel parameters of SMC
• Microcanonical Langevin Monte Carlo
• How to analyze the results of your MCLMC run

Models

• Change of Variable in HMC
• Gaussian Regression with the Elliptical Slice Sampler
• Bayesian Regression With Latent Gaussian Sampler
• Sparse regression
• Bayesian Logistic Regression
• Bayesian Logistic Regression With Latent Gaussian Sampler
• MLP classifier
• Hierarchical Bayesian Neural Networks
• Regime switching Hidden Markov model
• Parameter estimation in ODE models with a probabilistic ODE solver