## 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 fisheries science and economics. The wide-ranging practical importance of MCMC has sparked an expansive and deep investigation into fundamental Markov chain theory. The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology. The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments. |

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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 |

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Handbook of Markov Chain Monte Carlo Steve Brooks,Andrew Gelman,Galin Jones,Xiao-Li Meng No preview available - 2011 |