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.

• The Sampling Book Project
• 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
  • Comparing SMC and Persistent Sampling
  • Microcanonical Langevin Monte Carlo
  • MEADS: Ensemble Adaptation for GHMC
  • The Late Adjusted Parallel Sampler (LAPS)
  • Low-Rank Mass Matrix Adaptation
• 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