# 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, slices, niter = 55000, nburn = 5000,
priors = NULL, mh = list(algorithm = "pseudolikelihood", bandwidth = 0.2),
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.

slices

Deprecated.

niter

The number of iterations of the algorithm to perform.

nburn

The number of iterations to discard as burn-in.

priors

A list of priors for the parameters of the model.

mh

A list of options for the Metropolis-Hastings algorithm.

truth

A matrix containing the ground truth for the pixel labels.

##### Value

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

mritc.bayes