|aChapman & Hall/CRC handbooks of modern statistical methods
|aIncludes bibliographical references and index.
|tFoundations, methodology, and algorithms.|tIntroduction to Markov chain Monte Carlo /|rCharles J. Geyer ;|tA short history of MCMC :|tsubjective recollections from incomplete data /|rChristian Robert and George Casella ;|tReversible jump MCMC /|rYanan Fan and Scott A. Sisson ;|tOptimal proposal distributions and adaptive MCMC /|rJeffrey S. Rosenthal ;|tMCMC using Hamiltonian dynamics /|rRadford M. Neal ;|tInference from simulations and monitoring convergence /|rAndrew Gelman and Kenneth Shirley ;|tImplementing MCMC :|testimating with confidence /|rJames M. Flegal and Galin L. Jones ;|tPerfection within reach :|texact MCMC sampling /|rRadu V. Craiu and Xiao-Li Meng ;|tSpatial point processes /|rMark Huber ;|tThe data augmentation algorithm :|ttheory and methodology /|rJames P. Hobert ;|tImportance sampling, simulated tempering, and umbrella sampling /|rCharles J. Geyer ;|tLikelihood-free MCMC /|rScott A. Sisson and Yanan Fan --|tApplications and case studies.|tMCMC in the analysis of genetic data on related individuals /|rElizabeth Thompson ;|tAn MCMC-based analysis of a multilevel model for functional MRI data /|rBrian Caffo ... [et al.] ;|tPartially collapsed Gibbs sampling and path-adaptive metropolis-Hastings in high-energy astrophysics /|rDavid A. van Dyk and Taeyoung Park ;|tPosterior exploration for computationally intensive forward models /|rDavid Higdon ... [et al.] ;|tStatistical ecology /|rRuth King ;|tGaussian random field models for spatial data /|rMurali Haran ;|tModeling preference changes via a hidden Markov item response theory model /|rJong Hee Park ;|tParallel Bayesian MCMC imputation for multiple distributed lag models :|ta case study in environmental epidemiology /|rBiran Caffo ... [et al] ;|tMCMC for state-space models /|rPaul Fearnhead ;|tMCMC in educational research /|rRoy Levy, Robert J. Mislevy, and John T. Behrens --|tApplications of MCMC in fisheries science /|rRussell B. Millar ;|tModel comparison and simulation for hierarchical models :|tanalyzing rural-urban migration in Thailand /|rFiliz Garip and Bruce Western.
"Handbook of Markov Chain Monte Carlo" brings together the major advances that have occurred in recent years while incorporating enough introductory material for new users of MCMC. Along with thorough coverage of the theoretical foundations and algorithmic and computational methodology, this comprehensive handbook includes substantial realistic case studies from a variety of disciplines. These case studies demonstrate the application of MCMC methods and serve as a series of templates for the construction, implementation, and choice of MCMC methodology.