gibbsGMM

Fit a mixture of Gaussians to the observed data.

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
Moores, Pettitt & Mengersen (2020) <doi:10.1007/978-3-030-42553-1_6> for an overview
and also to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for
further details of specific algorithms.

Matt Moores

bayesImageS

Bayesian Methods for Image Segmentation using a Potts Model

Dai Feng

Kerrie Mengersen

gibbsGMM function

<dl><dt>y</dt>
<dd>A vector of observed pixel data.</dd>
<dt>niter</dt>
<dd>The number of iterations of the algorithm to perform.</dd>
<dt>nburn</dt>
<dd>The number of iterations to discard as burn-in.</dd>
<dt>priors</dt>
<dd>A list of priors for the parameters of the model.</dd></dl>

Arguments

Fit a mixture of Gaussians to the observed data. — gibbsGMM

<dl>

<dt>y</dt>
<dd>A vector of observed pixel data.</dd>


<dt>niter</dt>
<dd>The number of iterations of the algorithm to perform.</dd>


<dt>nburn</dt>
<dd>The number of iterations to discard as burn-in.</dd>


<dt>priors</dt>
<dd>A list of priors for the parameters of the model.</dd>

</dl>

gibbsGMM: Fit a mixture of Gaussians to the observed data.

Description

Usage

Value

Arguments