Markov Chain Monte Carlo in Practice
Regular price
€198.40
14 days return policy
Shipping & Delivery
advanced MCMC applications in research
Arm
Bayes Factor
Bayesian inference
Category=PBT
Category=PBWL
computational statistics
Conditional Expectation
disease mapping
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Full Conditional
Full Conditional Distributions
genetic data analysis
Gibbs Sampler
hierarchical models
Image Model
Independence Sampler
Markov Chain
Markov Chain Simulation
MCMC Algorithm
MCMC Estimation
MCMC Method
MCMC Output
MCMC Run
MCMC Sample
MCMC Scheme
Metropolis Hastings Algorithm
Metropolis Hastings Step
Missing Data
Monte Carlo Integration
Positive Recurrent
Posterior Distribution
statistical modeling
Stochastic Em
Swendsen Wang Algorithm
Product details
- ISBN 9780412055515
- Weight: 1090g
- Dimensions: 156 x 234mm
- Publication Date: 01 Dec 1995
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
Secure
checkout
Fast Shipping
Easy returns
In a family study of breast cancer, epidemiologists in Southern California increase the power for detecting a gene-environment interaction. In Gambia, a study helps a vaccination program reduce the incidence of Hepatitis B carriage. Archaeologists in Austria place a Bronze Age site in its true temporal location on the calendar scale. And in France, researchers map a rare disease with relatively little variation.
Each of these studies applied Markov chain Monte Carlo methods to produce more accurate and inclusive results. General state-space Markov chain theory has seen several developments that have made it both more accessible and more powerful to the general statistician. Markov Chain Monte Carlo in Practice introduces MCMC methods and their applications, providing some theoretical background as well. The authors are researchers who have made key contributions in the recent development of MCMC methodology and its application.
Considering the broad audience, the editors emphasize practice rather than theory, keeping the technical content to a minimum. The examples range from the simplest application, Gibbs sampling, to more complex applications. The first chapter contains enough information to allow the reader to start applying MCMC in a basic way. The following chapters cover main issues, important concepts and results, techniques for implementing MCMC, improving its performance, assessing model adequacy, choosing between models, and applications and their domains.
Markov Chain Monte Carlo in Practice is a thorough, clear introduction to the methodology and applications of this simple idea with enormous potential. It shows the importance of MCMC in real applications, such as archaeology, astronomy, biostatistics, genetics, epidemiology, and image analysis, and provides an excellent base for MCMC to be applied to other fields as well.
W.R. Gilks Institute of Public Health, Cambridge, UK; S. Richardson Imperial College, London, UK; David Spiegelhalter MRC Biostatistics Unit, Cambridge, UK.
