Statistical Inference and Simulation for Spatial Point Processes

Jesper Moller, Rasmus Plenge Waagepetersen

September 25, 2003 by Chapman and Hall/CRC
Reference - 320 Pages - 45 B/W Illustrations
ISBN 9781584882657 - CAT# C2654
Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

USD$125.95

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Features

  • Offers the first up-to-date, unified collection of theoretical advances and applications in simulation-based inference for spatial point processes
  • Devotes considerable attention to different facets of approximate likelihood inference and simulation-based Bayesian inference
  • Discusses perfect simulation procedures-an exciting new development in MCMC
  • Provides background material, including measure theory, in detailed appendices
  • Summary

    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.