simSW

Simulations for a \(100 \times 100\) lattice for fixed values
of the inverse temperature parameter, \(\beta\).

datasets

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 Bayesian indirect likelihood (BIL). Refer to <doi:10.1007/s11222-014-9525-6>
and <arXiv:1503.08066> for further details.

Matt Moores

bayesImageS

Bayesian Methods for Image Segmentation using a Potts Model

Kerrie Mengersen

Dai Feng

simSW function

A <code>list</code> containing 3 variables:<dl class="dl-horizontal">
 <dt>beta</dt><dd>values of beta</dd>
 <dt>matu</dt><dd>corresponding simulations of the sufficient statistic</dd>
 <dt>tm</dt><dd>time taken for these simulations</dd>
</dl>

Format

Simulation from the Potts model using Swendsen-Wang. — simSW

A <code>list</code> containing 3 variables:<dl class='dl-horizontal'>
 <dt>beta</dt><dd>values of beta</dd>
 <dt>matu</dt><dd>corresponding simulations of the sufficient statistic</dd>
 <dt>tm</dt><dd>time taken for these simulations</dd>
</dl>

simSW: Simulation from the Potts model using Swendsen-Wang.

Description

Usage

Arguments

Format

See Also