The Iterative Gaussian Markov Random Field Sampler is similar to the
Gibbs Sampler, but instead of the binomial distribution, as shown in
step 3.2 of Algorithm 1, we use the continuous
Gaussian Distribution as the probability function. For a
neighborhood model **N**, the conditional probability function for a
GMRF is:

where { } is the set of parameters specifying the model, and is the variance of a zero mean noise sequence.

An efficient parallel implementation is straightforward and similar to that of the Gibbs Sampler (Algorithm 1). Also, its analysis is identical to that provided for Gibbs Sampler.

David A. Bader

dbader@umiacs.umd.edu