Within this Book Full site

Metrics

Views
297

Filter my results

ISBN of the Book

Material or Process Book or Chapter Title Author or Editor Publication dates

Handbook of Markov Chain Monte Carlo

Edited by: Steve Brooks , Andrew Gelman , Galin L. Jones , Xiao-Li Meng

Print publication date:  May  2011
Online publication date:  May  2011

Print ISBN: 9781420079418
eBook ISBN: 9781420079425
Adobe ISBN:

10.1201/b10905
 Cite  Marc Record

Book description

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.

Table of contents

Chapter  1:  Introduction to Markov Chain Monte Carlo Download PDF
Chapter  2:  A Short History of MCMC: Subjective Recollections from Incomplete Data Download PDF
Chapter  3:  Reversible Jump MCMC Download PDF
Chapter  4:  Optimal Proposal Distributions and Adaptive MCMC Download PDF
Chapter  5:  MCMC Using Hamiltonian Dynamics Download PDF
Chapter  6:  Inference from Simulations and Monitoring Convergence Download PDF
Chapter  7:  Implementing MCMC: Estimating with Confidence Download PDF
Chapter  8:  Perfection within Reach: Exact MCMC Sampling Download PDF
Chapter  9:  Spatial Point Processes Download PDF
Chapter  10:  The Data Augmentation Algorithm: Theory and Methodology Download PDF
Chapter  11:  Importance Sampling, Simulated Tempering, and Umbrella Sampling Download PDF
Chapter  12:  Likelihood-Free MCMC Download PDF
Chapter  13:  MCMC in the Analysis of Genetic Data on Related Individuals Download PDF
Chapter  14:  An MCMC-Based Analysis of a Multilevel Model for Functional MRI Data Download PDF
Chapter  15:  Partially Collapsed Gibbs Sampling and Path-Adaptive Metropolis–Hastings in High-Energy Astrophysics Download PDF
Chapter  16:  Posterior Exploration for Computationally Intensive Forward Models Download PDF
Chapter  17:  Statistical Ecology Download PDF
Chapter  18:  Gaussian Random Field Models for Spatial Data Download PDF
Chapter  19:  Modeling Preference Changes via a Hidden Markov Item Response Theory Model Download PDF
Chapter  20:  Parallel Bayesian MCMC Imputation for Multiple Distributed Lag Models Download PDF
Chapter  21:  MCMC for State–Space Models Download PDF
Chapter  22:  MCMC in Educational Research Download PDF
Chapter  23:  Applications of MCMC in Fisheries Science Download PDF
Chapter  24:  Model Comparison and Simulation for Hierarchical Models: Analyzing Rural–Urban Migration in Thailand Download PDF
prelims Download PDF
Index Download PDF
Search for more...

Back to top

Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.