Bayesian Methods for Image Segmentation using a Potts Model

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Documentation for package ‘bayesImageS’ version 0.6-1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

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.