Gaussian Markov Random Fields: Theory and Applications / Edition 1

by Havard Rue, Leonhard Held

Overview

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects.

This book includes extensive case-studies and, online, a c-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important.

Product Details

ISBN-13:	9781584884323
Publisher:	Taylor & Francis
Publication date:	02/18/2005
Series:	Chapman & Hall/CRC Monographs on Statistics and Applied Probability , #104
Pages:	280
Product dimensions:	6.00(w) x 9.00(h) x (d)

About the Author

Havard Rue, Leonhard Held

Table of Contents

Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics, a very active area of research in which few up-to-date reference works are available. Gaussian Markov Random Field: Theory and Applications is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects. The book includes extensive case studies and online a c-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important.

Editorial Reviews

"I thus enjoyed reading this book and I would recommend it to anyone involved in spatial modelling as a time-effective introduction to the field, including a concern for practical implementation that may be lacking elsewhere and a good stylistic balance between background and technicalities, between bases and illustrations that make it a rather easy reading."

– Christian P. Robert, Université Paris, in Statistics in Medicine, 2006, Vol. 25

From the Publisher