# bayesImageS v0.3-3

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## Bayesian Methods for Image Segmentation using a Potts Model

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. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, and approximate Bayesian computation (ABC).

Various algorithms for segmentation of 2D and 3D images, such as computed tomography and satellite remote sensing. The R package bayesImageS implements Bayesian image analysis using the hidden Potts model with external field prior. Latent labels are sampled using chequerboard updating or Swendsen-Wang. Algorithms for the smoothing parameter include pseudolikelihood, path sampling, the exchange algorithm, and approximate Bayesian computation.

## Functions in bayesImageS

 Name Description gibbsGMM Fit a mixture of Gaussians to the observed data. bayesImageS Package bayesImageS swNoData Simulate pixel labels using the Swendsen-Wang algorithm. initSedki Initialize the ABC algorithm using the method of Sedki et al. (2013) smcPotts Fit the hidden Potts model using approximate Bayesian computation with sequential Monte Carlo (ABC-SMC). mcmcPottsNoData Simulate pixel labels using chequerboard Gibbs sampling. mcmcPotts Fit the hidden Potts model using a Markov chain Monte Carlo algorithm. testResample Test the residual resampling algorithm. gibbsNorm Fit a univariate normal (Gaussian) distribution to the observed data. sufficientStat Calculate the sufficient statistic of the Potts model for the given labels. exactPotts Calculate the distribution of the Potts model using a brute force algorithm. No Results!