Brooks / Gelman / Jones Handbook of Markov Chain Monte Carlo

1. Auflage 2011
ISBN: 978-1-4200-7942-5
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 619 Seiten

Reihe: Chapman & Hall/CRC Handbooks of Modern Statistical Methods

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.

Zielgruppe

Statisticians, advanced graduate students in statistics, new statistical researchers, and quantitatively oriented empirical researchers.

Autoren/Hrsg.

Brooks, Steve

Gelman, Andrew

Jones, Galin

Meng, Xiao-Li

Fachgebiete

• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung

Inhaltsverzeichnis

Foreword Stephen P. Brooks, Andrew Gelman, Galin L. Jones, and Xiao-Li Meng
Introduction to MCMC, Charles J. Geyer
A short history of Markov chain Monte Carlo: Subjective recollections from in-complete data, Christian Robert and George Casella
Reversible jump Markov chain Monte Carlo, Yanan Fan and Scott A. Sisson
Optimal proposal distributions and adaptive MCMC, Jeffrey S. Rosenthal
MCMC using Hamiltonian dynamics, Radford M. Neal
Inference and Monitoring Convergence, Andrew Gelman and Kenneth Shirley
Implementing MCMC: Estimating with confidence, James M. Flegal and Galin L. Jones
Perfection within reach: Exact MCMC sampling, Radu V. Craiu and Xiao-Li Meng
Spatial point processes, Mark Huber
The data augmentation algorithm: Theory and methodology, James P. Hobert
Importance sampling, simulated tempering and umbrella sampling, Charles J.Geyer
Likelihood-free Markov chain Monte Carlo, Scott A. Sisson and Yanan Fan
MCMC in the analysis of genetic data on related individuals, Elizabeth Thompson
A Markov chain Monte Carlo based analysis of a multilevel model for functional MRI data, Brian Caffo, DuBois Bowman, Lynn Eberly, and Susan Spear Bassett
Partially collapsed Gibbs sampling & path-adaptive Metropolis-Hastings in high-energy astrophysics, David van Dyk and Taeyoung Park
Posterior exploration for computationally intensive forward models, Dave Higdon, C. Shane Reese, J. David Moulton, Jasper A. Vrugt and Colin Fox
Statistical ecology, Ruth King
Gaussian random field models for spatial data, Murali Haran
Modeling preference changes via a hidden Markov item response theory model, Jong Hee Park
Parallel Bayesian MCMC imputation for multiple distributed lag models: A case study in environmental epidemiology, Brian Caffo, Roger Peng, Francesca Dominici, Thomas A. Louis, and Scott Zeger
MCMC for state space models, Paul Fearnhead
MCMC in educational research, Roy Levy, Robert J. Mislevy, and John T. Behrens
Applications of MCMC in fisheries science, Russell B. Millar
Model comparison and simulation for hierarchical models: analyzing rural-urban migration in Thailand, Filiz Garip and Bruce Western