: Updated material on convergence bounds and control variates to help practitioners ensure their simulations are reliable. Target Audience Analyzing Markov chain Monte Carlo output - Vats - 2020
: Coverage of integrating MCMC with deep learning and machine learning approaches, along with guidance for implementation on modern hardware.
: Includes ten entirely new chapters on modern breakthroughs like unbiased MCMC methods , multi-modal sampling , and involutive MCMC theory . Handbook of Markov Chain Monte Carlo
A revised second edition, featuring new editors and Dootika Vats , reflects the rapid evolution of the field since 2011:
The handbook is typically divided into two distinct sections to balance theory with practical execution: : Updated material on convergence bounds and control
: The first half covers the "why" and "how" of MCMC, including basic theory and standard algorithms like Metropolis-Hastings and the Gibbs sampler .
The is a definitive reference for developers and practitioners in the field of statistical computing. Edited by a "world-class" team including Steve Brooks , Andrew Gelman , Galin Jones , and Xiao-Li Meng , it serves as a successor to earlier foundational texts, providing a modern, comprehensive look at MCMC technology. Core Structure and Content A revised second edition, featuring new editors and
: The second half features diverse case studies across fields such as astrophysics , ecology , sociology , and brain imaging , demonstrating how MCMC solves realistic scientific problems. Key Features of the 2nd Edition