gibbsPotts {bayesImageS}

Fit a hidden Potts model to the observed data, using a fixed value of beta.

Description

Fit a hidden Potts model to the observed data, using a fixed value of beta.

Usage

gibbsPotts(y, labels, beta, mu, sd, neighbors, blocks, priors, niter = 1)

Arguments

y

A vector of observed pixel data.

labels

A matrix of pixel labels.

beta

The inverse temperature parameter of the Potts model.

mu

A vector of means for the mixture components.

sd

A vector of standard deviations for the mixture components.

neighbors

A matrix of all neighbors in the lattice, one row per pixel.

blocks

A list of pixel indices, dividing the lattice into independent blocks.

priors

A list of priors for the parameters of the model.

niter

The number of iterations of the algorithm to perform.

Value

A matrix containing MCMC samples for the parameters of the Potts model.