Handbook of Markov Chain Monte CarloSteve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng Since their popularization in the 1990s, Markov chain Monte Carlo (MCMC) methods have revolutionized statistical computing and have had an especially profound impact on the practice of Bayesian statistics. Furthermore, MCMC methods have enabled the development and use of intricate models in an astonishing array of disciplines as diverse as fisherie |
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Contents
| 3 | |
Subjective Recollections from Incomplete Data | 49 |
Chapter 3 Reversible Jump MCMC | 67 |
Chapter 4 Optimal Proposal Distributions and Adaptive MCMC | 93 |
Chapter 5 MCMC Using Hamiltonian Dynamics | 113 |
Chapter 6 Inference from Simulations and Monitoring Convergence | 163 |
Estimating with Confidence | 175 |
Exact MCMC Sampling | 199 |
Chapter 14 An MCMCBased Analysis of a Multilevel Model for Functional MRI Data | 363 |
Chapter 15 Partially Collapsed Gibbs Sampling and PathAdaptive MetropolisHastings in HighEnergy Astrophysics | 383 |
Chapter 16 Posterior Exploration for Computationally Intensive Forward Models | 401 |
Chapter 17 Statistical Ecology | 419 |
Chapter 18 Gaussian Random Field Models for Spatial Data | 449 |
Chapter 19 Modeling Preference Changes via a Hidden MarkovItem Response Theory Model | 479 |
A Case Study in Environmental Epidemiology | 493 |
Chapter 21 MCMC for StateSpace Models | 513 |
Chapter 9 Spatial Point Processes | 227 |
Theory and Methodology | 253 |
Chapter 11 Importance Sampling Simulated Tempering and Umbrella Sampling | 295 |
Chapter 12 LikelihoodFree MCMC | 313 |
Part II Applications and Case Studies | 337 |
Chapter 13 MCMC in the Analysis of Genetic Dataon Related Individuals | 339 |
Other editions - View all
Handbook of Markov Chain Monte Carlo Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng No preview available - 2011 |
Common terms and phrases
acceptance probability acceptance rate American Statistical Association analysis applications approach approximation asymptotic batch means Bayes factors Bayesian Bayesian inference burn-in calculation chain Monte Carlo components computation conditional distribution consider convergence covariance denote density dependence discussion draw dynamics efficient Equation ergodic error estimates example Figure function Gaussian Geyer Gibbs sampler given Hamiltonian Hence ideal points implementation independent inference iterations Journal kernel latent variables leapfrog steps likelihood likelihood-free linear marginal Markov chain Markov chain Monte matrix MCMC MCMC algorithm mixing Monte Carlo methods multivariate normal observed optimal parameters plots point processes posterior distribution posterior mean posterior probability prior problem proposal distribution PX-DA algorithm random effects random-walk Metropolis regression reversible jump Royal Statistical Society Section SGLMs simulation single-site spatial standard deviation stepsize target distribution theorem trajectory unnormalized update variance vector zero
Bibliographic information
| Title | Handbook of Markov Chain Monte Carlo Chapman & Hall/CRC Handbooks of Modern Statistical Methods |
| Editors | Steve Brooks, Andrew Gelman, Galin Jones, Xiao-Li Meng |
| Edition | illustrated |
| Publisher | CRC Press, 2011 |
| ISBN | 1420079425, 9781420079425 |
| Length | 619 pages |
| Subjects | › › Mathematics / Probability & Statistics / General |
| Export Citation | BiBTeX EndNote RefMan |