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.
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Rue and Held have constructed an impressive reference devoted to the theory and applications of Gaussian Markov random fields (GMRFs), with emphasis on recent computational development, in an accessible format. ...After a sequence of contributions by the authors and their colleagues covering a wide range of applications, a reference on the basic theory of GMRFs and state-of-the-art of the computational development and applications was needed. Gaussian Markov Random Fields: Theory and Applications is exactly such a book. I can recommend this book as a very good graduate level textbook. I also believe that readers will learn much from the GMRFlib software.
-Journal of the American Statistical Association
The authors succeed in their attempt 'to provide the first comprehensive account of the main properties of GMRFs, to emphasize the strong connection between GMRFs and numerical methods for sparse matrices, and to outline various applications of GMRFs to statistical inference.' ...We believe that this book will be useful for numerous students and researchers in statistics and applied probability.
-Mathematical Reviews
Its strengths are, among others, the quality of the exposition, the attention to numerical problems and their resolution, and the wealth of illustrations. ...I would recommend it to anyone involved in spatial modeling 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 makes it a rather easy reading.
Christian P. Robert, Statistics in Medicine
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