Statistical Inference and Simulation for Spatial Point Processes

"This book is an extremely well-written summary of important topics in the analysis of spatial point processes. … The authors do an excellent job focusing on those theoretical concepts and methods that are most important in applied research. Although other good books on spatial point processes are available, this is the first text to tackle difficult issues of simulation-based inference for such processes … . [T]he text … is remarkably easy to follow. … The authors have a very impressive knack for explaining complicated topics very clearly … . [This book] will no doubt prove an outstanding resource for researchers and students … Its excellent survey of the vast array of models is reason enough to own it. As computer technology and speed advance … the authors' clear, detailed, and comprehensive survey of simulation methods for spatial point processes will become increasingly important."- Journal of the American Statistical Association"… [T]his monograph is a well-written and concisely presented journey through the primary types of spatial point process frameworks. There is a useful equal balance between theoretical development and inference centred on simulation-based methods. … This volume would be well suited for library purchase. … [A] worthwhile investment."- Journal of the Royal Statistics Society"The book is very well organized and clearly written. It provides both an introduction and a review of the subject in a very condensed form. Thus it is an excellent support for a systematic approach to and an orientation for the current extensive literature with its different branches."-Mathematical Reviews Issue 2004"This book provides an excellent and up-to-date review of developments in this area. It covers most, if not all, of the major classes of models, and discusses methods for their approximate and exact simulation."-ISI Short Book Reviews, Aug 04"The book is a landmark in the development of point process statistics and sets standards in its field. It will be the key reference for all which is related to simulation in point process statistics."- Dietrich Stoyan, Institut für Stochastik, Begakademie, Freiberg, Germany, in Statistics in Medicine, 2004"Well and clearly written…self-contained…accessible to a wide audience."-Zentralblatt MATH 1044

• Introduction. Background. Markov Chain Monte Carlo Algorithms for Spatial Point Processes. Perfect Simulation. Approximate Likelihood Inference. Simulation-Based Bayesian Inference.