mcmcPotts

0th

Percentile

Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.

Fit the hidden Potts model using a Markov chain Monte Carlo algorithm.

Usage
mcmcPotts(y, neighbors, blocks, priors, mh, niter = 55000,
nburn = 5000, truth = NULL)
Arguments
y

A vector of observed pixel data.

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.

mh

A list of options for the Metropolis-Hastings algorithm.

niter

The number of iterations of the algorithm to perform.

nburn

The number of iterations to discard as burn-in.

truth

A matrix containing the ground truth for the pixel labels.

Value

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

Aliases
• mcmcPotts
Documentation reproduced from package bayesImageS, version 0.6-0, License: GPL (>= 2) | file LICENSE

Community examples

Looks like there are no examples yet.