Gaussian Markov Random Fields
Theory and Applications
Published on 18. February 2005
280 pages
978-1-4987-1867-7 (ISBN)
Description
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-studie
Reviews / Votes
"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
More details
Series
Language
English
Place of publication
Philadelphia, PA
United States
Publishing group
Taylor & Francis Inc
Target group
Professional and scholarly
Product notice
Reflowable
File size
34,24 MB
ISBN-13
978-1-4987-1867-7 (9781498718677)
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Schweitzer Classification
Other editions
Additional editions
Book
01/2023
1st Edition
Chapman & Hall/CRC
€64.00
Shipment within 10-20 days
Book
02/2005
1st Edition
Chapman & Hall/CRC
€232.00
Shipment within 15-20 days
Persons
Content
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.
