generatePriorPsi

generate prior value for parameter Psi

Model-based clustering using Bayesian parsimonious Gaussian mixture models.
MCMC (Markov chain Monte Carlo) are used for parameter estimation. The RJMCMC (Reversible-jump Markov chain Monte Carlo) is used for model selection.
GREEN et al. (1995) <doi:10.1093/biomet/82.4.711>.

Yaoxiang Li

bpgmm

Bayesian Model Selection Approach for Parsimonious Gaussian
Mixture Models

Xiang Lu

Tanzy Love

generatePriorPsi function

<dl><dt>p</dt>
<dd>the number of features</dd>
<dt>m</dt>
<dd>the number of clusters</dd>
<dt>delta</dt>
<dd>hyperparameters</dd>
<dt>bbeta</dt>
<dd>hyperparameters</dd>
<dt>constraint</dt>
<dd>the pgmm constraint, a vector of length three with binary entry. For example, c(1,1,1) means the fully constraint model</dd></dl>

Arguments

generatePriorPsi — generatePriorPsi

<dl>

<dt>p</dt>
<dd>the number of features</dd>


<dt>m</dt>
<dd>the number of clusters</dd>


<dt>delta</dt>
<dd>hyperparameters</dd>


<dt>bbeta</dt>
<dd>hyperparameters</dd>


<dt>constraint</dt>
<dd>the pgmm constraint, a vector of length three with binary entry. For example, c(1,1,1) means the fully constraint model</dd>

</dl>

generatePriorPsi: generatePriorPsi

Description

Usage

Arguments