Handbook of Markov Chain Monte Carlo

This handbook is an edited, multi-author volume that assembles a broad set of chapters on Markov chain Monte Carlo (MCMC) methods. It opens with introductory material and a short history of MCMC, then progresses through core methodological topics such as reversible jump MCMC, optimal proposal distributions and adaptive MCMC, MCMC using Hamiltonian dynamics, inference and convergence monitoring, estimating with confidence, exact MCMC sampling, importance sampling with simulated tempering and umbrella sampling, and likelihood-free MCMC.

Alongside its methodological chapters, the handbook devotes substantial attention to applications across many scientific domains. These include analysis of genetic data on related individuals, multilevel modeling of functional MRI data, high-energy astrophysics, statistical ecology, Gaussian random field models for spatial data, item response modeling of preference changes, distributed lag models in environmental epidemiology, state space models, educational research, fisheries science, and hierarchical models of migration. This breadth made the volume a practical reference for researchers and software developers working with MCMC.