Gaussian Markov Random Fields

Theory and Applications

Havard Rue, Leonhard Held, Leonhard Held (University of Munich, Munich, Germany) , Valerie Isham

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English

CRC Press Inc
18 February 2005

Probability & statistics; Stochastics; Mathematical & statistical software

Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability

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.

By: Havard Rue, Leonhard Held, Leonhard Held (University of Munich, Munich, Germany)
Series edited by: Valerie Isham, R.J. Tibshirani (Stanford university Stanford University Stanford University, California, USA Stanford University, California, USA)
Imprint: CRC Press Inc
Country of Publication: United States
Volume: 104
Dimensions: Height: 229mm, Width: 152mm, Spine: 21mm
Weight: 544g
ISBN: 9781584884323
ISBN 10: 1584884320
Series: Chapman & Hall/CRC Monographs on Statistics and Applied Probability
Pages: 280
Publication Date: 18 February 2005
Audience: Professional and scholarly , Undergraduate
Format: Hardback
Publisher's Status: Active

Havard Rue, Leonhard Held

Reviews for Gaussian Markov Random Fields: Theory and Applications

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, Universite Paris, in Statistics in Medicine, 2006, Vol. 25