fitStan

Results from fitting the surrogate model in Stan.

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

fitStan function

A <code>list</code> containing 3 variables:<dl class="dl-horizontal">
 <dt>summary</dt><dd>summary of the model fit for all chains merged</dd>
 <dt>c_summary</dt><dd>summary for each HMC chain individually</dd>
 <dt>tm</dt><dd>time taken to fit the model</dd>
</dl>

Format

Results from fitting the surrogate model in Stan. — fitStan

A <code>list</code> containing 3 variables:<dl class='dl-horizontal'>
 <dt>summary</dt><dd>summary of the model fit for all chains merged</dd>
 <dt>c_summary</dt><dd>summary for each HMC chain individually</dd>
 <dt>tm</dt><dd>time taken to fit the model</dd>
</dl>

fitStan: Results from fitting the surrogate model in Stan.

Description

Usage

Arguments

Format

See Also