mcmcPotts

A matrix of all neighbors in the lattice, one row per pixel.

neighbors

A list of pixel indices, dividing the lattice into independent blocks.

blocks

A list of priors for the parameters of the model.

priors

A list of options for the Metropolis-Hastings algorithm.

The number of iterations of the algorithm to perform.

niter

The number of iterations to discard as burn-in.

nburn

A matrix containing the ground truth for the pixel labels.

truth

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

Various algorithms for segmentation of 2D and 3D images, such
as computed tomography and satellite remote sensing. This package implements
Bayesian image analysis using the hidden Potts model with external field
prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>.
Latent labels are sampled using chequerboard updating or Swendsen-Wang.
Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to
<doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for further details.

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

Description

Usage

Arguments

Value