ABSTRACT
This monograph considers Gaussian Markov random fields (GMRFs) covering both theory and applications. A GMRF is really a simple construct: It is just a (finite-dimensional) random vector following a multivariate normal (or Gaussian) distribution. However, we will be concerned with more restrictive versions where the GMRF satisfies additional conditional independence assumptions, hence the term Markov.
