Spatial statistics and Markov Chain Monte Carlo (MCMC) techniques have
each undergone major developments in the last decade. Also, these two
areas are mutually reinforcing, because MCMC methods are often
necessary for the practical implementation of spatial statistical
inference, while new spatial stochastic models in turn motivate the
development of improved MCMC algorithms.
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 consists of
four chapters: 1. Petros Dellaportas and Gareth O. Roberts give a
tutorial on MCMC methods, the computational methodology which is
essential for virtually all the complex spatial models to be
considered in subsequent chapters. 2. Peter J. Diggle, Paulo J,
Ribeiro Jr., and Ole F. Christensen introduce the reader to the model-
based approach to geostatistics, i.e. the application of general
statistical principles to the formulation of explicit stochastic
models for geostatistical data, and to inference within a declared
class of models. 3. Merrilee A. Hurn, Oddvar K. Husby, and H?vard Rue
discuss various aspects of image analysis, ranging from low to high
level tasks, and illustrated with different examples of applications.
4. Jesper Moller and Rasmus P. Waggepetersen collect recent
theoretical advances in simulation-based inference for spatial point
processes, and discuss some examples of applications.
The volume introduces topics of current interest in spatial and
computational statistics, which should be accessible to postgraduate
students as well as to experienced statistical researchers. It is
partly based on the course material for the "TMR and MaPhySto Summer
School on Spatial Statistics and Computational Methods," held at
Aalborg University, Denmark, August 19-22, 2001.
Reviews / Votes
"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
Series
Edition
Language
Place of publication
Target group
Professional and scholarly
Research
Illustrations
121 s/w Abbildungen
XIV, 205 p. 121 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 13 mm
Weight
ISBN-13
978-0-387-00136-4 (9780387001364)
DOI
10.1007/978-0-387-21811-3
Schweitzer Classification
1 An Introduction to MCMC.- 2 An Introduction to Model-Based Geostatistics.- 3 A Tutorial on Image Analysis.- 4 An Introduction to Simulation-Based Inference for Spatial Point Processes.
