Researchers in spatial statistics and image analysis are familiar
with Gaussian Markov Random Fields (GMRFs), and they are
traditionally among the few who use them. There are, however, a
wide range of applications for this methodology, from structural
time-series analysis to the analysis of longitudinal and survival
data, spatio-temporal models, graphical models, and semi-parametric
statistics. With so many applications and with such widespread use
in the field of spatial statistics, it is surprising that there
remains no comprehensive reference on the subject.
Gaussian Markov Random Fields: Theory and Applications provides
such a reference, using a unified framework for representing and
understanding GMRFs. Various case studies illustrate the use of
GMRFs in complex hierarchical models, in which statistical
inference is only possible using Markov Chain Monte Carlo (MCMC)
techniques. The preeminent experts in the field, the authors
emphasize the computational aspects, construct fast and reliable
algorithms for MCMC inference, and provide an online C-library for
fast and exact simulation.
This is an ideal tool for researchers and students in
statistics, particularly biostatistics and spatial statistics, as
well as quantitative researchers in engineering, epidemiology,
image analysis, geography, and ecology, introducing them to this
powerful statistical inference method.
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