# Bayesian Methods for Image Segmentation using a Potts Model

## Documentation for package ‘bayesImageS’ version 0.6-1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

bayesImageS | Package bayesImageS
exactPotts | Calculate the distribution of the Potts model using a brute force algorithm.
getBlocks | Get Blocks of a Graph
getEdges | Get Edges of a Graph
getNeighbors | Get Neighbours of All Vertices of a Graph
gibbsGMM | Fit a mixture of Gaussians to the observed data.
gibbsNorm | Fit a univariate normal (Gaussian) distribution to the observed data.
gibbsPotts | Fit a hidden Potts model to the observed data, using a fixed value of beta.
initSedki | Initialize the ABC algorithm using the method of Sedki et al. (2013)
mcmcPotts | Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.
mcmcPottsNoData | Simulate pixel labels using chequerboard Gibbs sampling.
res | Simulation from the Potts model using single-site Gibbs updates.
res2 | Simulation from the Potts model using single-site Gibbs updates.
res3 | Simulation from the Potts model using single-site Gibbs updates.
res4 | Simulation from the Potts model using single-site Gibbs updates.
res5 | Simulation from the Potts model using single-site Gibbs updates.
smcPotts | Fit the hidden Potts model using approximate Bayesian computation with sequential Monte Carlo (ABC-SMC).
sufficientStat | Calculate the sufficient statistic of the Potts model for the given labels.
swNoData | Simulate pixel labels using the Swendsen-Wang algorithm.
synth | Simulation from the Potts model using Swendsen-Wang.
testResample | Test the residual resampling algorithm.