Statistical inference and simulation for spatial point processes
著者
書誌事項
Statistical inference and simulation for spatial point processes
（Monographs on statistics and applied probability, 100）
Chapman & Hall/CRC, c2004
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注記
Bibliography: p. 279-292
Includes indexes
内容説明・目次
内容説明
Spatial point processes play a fundamental role in spatial statistics and today they are an active area of research with many new applications. Although other published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and a thorough treatment of the theory and applications of simulation-based inference is difficult to find. Written by researchers at the top of the field, this book collects and unifies recent theoretical advances and examples of applications. The authors examine Markov chain Monte Carlo algorithms and explore one of the most important recent developments in MCMC: perfect simulation procedures.
目次
Introduction. Background. Markov Chain Monte Carlo Algorithms for Spatial Point Processes. Perfect Simulation. Approximate Likelihood Inference. Simulation-Based Bayesian Inference.
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