Table of Contents

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

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About the Author

W.R. Gilks Institute of Public Health, Cambridge, UK; S. Richardson Imperial College, London, UK; David Spiegelhalter MRC Biostatistics Unit, Cambridge, UK.