📚 The MCMC Handbook Has a Second Edition — And It’s Available for Free#
Andrew Gelman and co-editors published the second edition of the Handbook of Markov Chain Monte Carlo. Most chapters are available on arXiv. 🎉
📖 Featured Chapters#
The book includes chapters on:
- ⚙️ MCMC on modern hardware (Ch. 24) — GPU, updated software
- ⏱️ How many iterations do I need? (Ch. 4) — by Gelman and Margossian
- 🔬 Diagnostics, convergence, and advanced algorithms
- 🧮 Markov chain methods for different contexts
🔗 Free Access on GitHub#
Dootika Vats set up a GitHub page with all chapters and links to their arXiv versions.
👥 Editors#
Radu Craiu, Dootika Vats, Galin Jones, Steve Brooks, Xiao-Li Meng, and Andrew Gelman.
💡 Explanation in a nutshell#
Markov Chain Monte Carlo (MCMC) is the central algorithm of modern Bayesian statistics: it allows approximating posterior distributions when the analytical solution doesn’t exist. This second edition of the Handbook covers everything from fundamentals to implementations on modern hardware, with chapters written by the field’s leading researchers. It’s an essential reference resource for statisticians, data scientists, and anyone working with complex probabilistic models — and most chapters are freely available.
More information at the link 👇
