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Informationen zum Autor W.R. Gilks Institute of Public Health, Cambridge, UK; S. Richardson Imperial College, London, UK; David Spiegelhalter MRC Biostatistics Unit, Cambridge, UK. Klappentext This work introduces Markov chain Monte Carlo methodology at a level suitable for applied statisticians. It explains the methodology and its theoretical background! summarizes application areas! and presents illustrative applications in many areas including archaeology and astronomy. Zusammenfassung This work introduces Markov chain Monte Carlo methodology at a level suitable for applied statisticians. It explains the methodology and its theoretical background, summarizes application areas, and presents illustrative applications in many areas including archaeology and astronomy. Inhaltsverzeichnis INTRODUCING MARKOV CHAIN MONTE CARLO; HEPATITIS B: A CASE STUDY IN MCMC METHODS; MARKOV CHAIN CONCEPTS RELATED TO SAMPLING ALGORITHMS; INTRODUCTION TO GENERAL STATE-SPACE MARKOV CHAIN THEORY; FULL CONDITIONAL DISTRIBUTIONS; STRATEGIES FOR IMPROVING MCMC; IMPLEMENTING MCMC; INFERENCE AND MONITORING CONVERGENCE; MODEL DETERMINATION USING SAMPLING-BASED METHODS; HYPOTHESIS TESTING AND MODEL SELECTION; MODEL CHECKING AND MODEL IMPROVEMENT; STOCHASTIC SEARCH VARIABLE SELECTION; BAYESIAN MODEL COMPARISON VIA JUMP DIFFUSIONS; ESTIMATION AND OPTIMIZATION OF FUNCTIONS; STOCHASTIC EM: METHOD AND APPLICATION; GENERALIZED LINEAR MIXED MODELS; HIERARCHICAL LONGITUDINAL MODELLING; MEDICAL MONITORING; MCMC FOR NONLINEAR HIERARCHICAL MODELS; BAYESIAN MAPPING OF DISEASE; MCMC IN IMAGE ANALYSIS; MEASUREMENT ERROR; GIBBS SAMPLING METHODS IN GENETICS; MIXTURES OF DISTRIBUTIONS: INFERENCE AND ESTIMATION; AN ARCHAEOLOGICAL EXAMPLE: RADIOCARBON DATING
