gibbsPotts: Fit a hidden Potts model to the observed data, using a fixed...

In bayesImageS: Bayesian Methods for Image Segmentation using a Potts Model

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.

bayesImageS documentation built on Nov. 6, 2025, 1:18 a.m.