Spatial statistics and computational methods空间统计学与计算方法

媒体关注与评论

"This book is a compilation of lecture notes from a number of distinguished professors from the United States, England, France, and Germany. This type of publication in statistics is specifically useful for postgraduate students and scientists." "Spatial Statistics and Computational Methods successfully presents and updates the recent theoretical advances accompanied by examples and applications in simulation-based inferences. This book will be of practical use for many readers, particularly graduate students." Technometrics, February 2004 "An impressive team of 10 experts wrote this book's four informative chapters on modern computational methods in spatial statistics...Spatial Statistics and Computational Methods...enjoys the clarity of organization and exposition that would make it a suitable main reference for a graduate course in modern methods in spatial statistics." Journal of the American Statistical Association, September 2004 "I found this book to be a valuable contribution...MCMC and spatial statistics have undergone major development over the past ten years. The tutorials covered in this book capture some of these developments and present them in a manner that is accessible to the statistically minded scientific community." Environmetrics Newsletter, 2004 "...Provides the reader with a very good overview of MCMC methodology...it can serve as a reading material for a graduate course that discusses these topics...Well-written chapter[s]." Journal of Statsitical Software, April 2005

书籍目录

Preface  Contributors  1 An Introduction to MCMC 　1.1 MCMC and spatial statistics 　1.2 The Gibbs sampler 　1.3 The Metropolis-Hastings algorithm 　1.4 MCMC Theory 　1.5 Practical implementation 　1.6 An illustrative example 　1.7 Appendix: Model determination using MCMC 2 An Introduction to Model-Based Geostatistics 　2.1 Introduction 　2.2 Examples of geostatistical problems 　2.3 The general geostatistical model 　2.4 The Gaussian Model 　2.5 Parametric estimation of covariance structure 　2.6 Plug-in prediction 　2.7 Bayesian inference for the linear Gaussian model 　2.8 A Case Study: the Swiss rainfall data 　2.9 Generalised linear spatial models 　2.10 Discussion 　2.11 Software 　2.12 Further reading 3 A Tutorial on Image Analysis 　3.1 Introduction 　3.2 Markov random field models 　3.3 Models for binary and categorical images 　3.4 Image estimators and the treatment of parameters 　3.5 Grey-level images 　3.6 High-level imaging 　3.7 An example in ultrasound imaging 4 An Introduction to Simulation-Based Inference for Spatial Point Processes 　4.1 Introduction 　4.2 Illustrating examples 　4.3 What is a spatial point process? 　4.4 Poisson point processes 　4.5 Summary statistics 　4.6 Models and simulation-based inference for aggregated point patterns 　4.7 Models and simulation-based inference for Markov point processes 　4.8 Further reading and concluding remarks Index

作者简介

This volume shows how sophisticated spatial statistical and computational methods apply to a range of problems of increasing importance for applications in science and technology. It introduces topics of current interest in spatial and computational statistics, which should be accessible to postgraduate students as well as to experienced statistical researchers.

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