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.
