Statistical inference and simulation for spatial point processes

Jesper Moller, Rasmus Plenge Waagepetersen

314 pages, parution le 22/10/2003

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Résumé

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

Spatial point processes play a fundamental role in spatial statistics and today they are a very active area of research with many new and emerging applications. Although published works address different aspects of spatial point processes, most of the classical literature deals only with nonparametric methods, and nowhere can one find a comprehensive treatment of the theory and applications of simulation-based inference. 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 (MCMC) algorithms and explore one of the most important recent developments in MCMC-perfect simulation procedures.

Examples of spatial point patterns
Introduction to point processes
Poisson point processes
Summary statistics
Cox processes
Markov point processes
Metropolis-hastings algorithms
Simulation-based inference
Inference for Markov point processes
Inference for Cox processes
Birth-death processes and perfect simulation
Appendices
References
Subject Index
Notation Index

Caractéristiques techniques

	PAPIER
Éditeur(s)	Chapman and Hall / CRC
Auteur(s)	Jesper Moller, Rasmus Plenge Waagepetersen
Parution	22/10/2003
Nb. de pages	314
Format	15,5 x 23,5
Couverture	Relié
Poids	555g
Intérieur	Noir et Blanc
EAN13	9781584882657
ISBN13	978-1-58488-265-7