This is the GitHub repo for the revised and expanded second edition of the Handbook of Markov Chain Monte Carlo. The second edition reflects the dramatic evolution of MCMC methods since the publication of the first edition. With the addition of two new editors, Radu V. Craiu and Dootika Vats, this comprehensive reference now offers deeper insights into the theoretical foundations and cutting-edge developments that are reshaping the field. Chapter details and available codes are available below.
Editors: Radu V. Caiu, Dootika Vats, Galin L. Jones, Steve Brooks, Andrew Gelman, Xiao-li Meng.
Table of contents for the Second Edition:
| S.No. | Chapter Name | Authors(s) | Code |
|---|---|---|---|
| 1. | Introduction to MCMC | Charles J. Geyer | |
| 2. | MCMC using Hamiltonian Dynamics | Radford Neal | |
| 3. | Optimising and Adapting Metropolis Algorithm Proposal Distributions | Jeffrey S. Rosenthal | |
| 4. | For how many iterations should we run Markov chain Monte Carlo? | Charles C. Margossian, Andrew Gelman | |
| 5. | Implementing MCMC: Multivariate estimation with confidence | James M. Flegal, Rebecca P. Kurtz-Garcia | |
| 6. | Importance Sampling, Simulated Tempering, and Umbrella Sampling | Charles J. Geyer | |
| 7. | Reversible jump Markov chain Monte Carlo and multi-model samplers | Yanan Fan, Scott A. Sisson, Laurence Davies | |
| 8. | Perfecting MCMC Sampling: Recipes and Reservations | Radu V. Craiu, Xiao-Li Meng | |
| 9. | The Data Augmentation Algorithm | Vivekananda Roy, Kshitij Khare, James P. Hobert | |
| 10. | Latent Gaussian Models and Computation for Large Spatial Data | Murali Haran, John Hughes, Ben Seiyon Lee | Code |
| 11. | Partially collapsed Gibbs sampling & path-adaptive Metropolis Hastings in high-energy astrophysics | David A. van Dyk, Taeyoung Park, Hector McKimm | |
| 12. | Posterior exploration for computationally intensive forward models | Mikkel B. Lykkegaard, Colin Fox, Dave Higdon, C. Shane Reese, J. David Moulton | |
| 13. | MCMC for State Space Models | Paul Fearnhead Chris Sherlock | |
| 14. | MCMC methods for multi-modal distributions | Krzysztof Łatuszyński, Matthew T. Moores, Timothée Stumpf-Fétizon | |
| 15. | Algorithms for Models with Intractable Normalizing Functions | Murali Haran, Bokgyeong Kang, Jaewoo Park | Code |
| 16. | Sacred and profane: from the involutive theory of MCMC to helpful Hamiltonian hacks | Nathan E. Glatt-Holtz, Andrew J. Holbrook, Justin A. Krometis, Cecilia F. Mondaini, Ami Sheth | Code |
| 17. | Unbiased Markov Chain Monte Carlo: what, why, and how | Yves F. Atchadé, Pierre E. Jacob | Code |
| 18. | Control Variates for MCMC | Leah South, Matthew Sutton | Code |
| 19. | Convergence Bounds for Monte Carlo Markov Chains | Qian Qin | |
| 20. | Perturbations of Markov Chains | Daniel Rudolf, Aaron Smith, Matias Quiroz | |
| 21. | Running Markov Chain Monte Carlo on Modern Hardware and Software | Pavel Sountsov, Colin Carroll, Matthew D. Hoffman | |
| 22. | Bayesian Computation in Deep Learning | Wenlong Chen, Bolian Li, Ruqi Zhang, Yingzhen Li | |
| 23. | MCMC-driven learning | Alexandre Bouchard-Côté, Trevor Campbell, Geoff Pleiss, Nikola Surjanovic |
