generatePriorThetaY

generate prior value for parameter Theta and Y.

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

generatePriorThetaY function

<dl><dt>m</dt>
<dd>the number of cluster</dd>
<dt>n</dt>
<dd>sample size</dd>
<dt>p</dt>
<dd>number of covariates</dd>
<dt>muBar</dt>
<dd>parameter</dd>
<dt>hparam</dt>
<dd>hyperparameters</dd>
<dt>qVec</dt>
<dd>the vector of the number of factors in each clusters</dd>
<dt>ZOneDim</dt>
<dd>ZOneDim</dd>
<dt>constraint</dt>
<dd>constraint</dd></dl>

Arguments

PriorThetaY list — generatePriorThetaY

<dl>

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


<dt>n</dt>
<dd>sample size</dd>


<dt>p</dt>
<dd>number of covariates</dd>


<dt>muBar</dt>
<dd>parameter</dd>


<dt>hparam</dt>
<dd>hyperparameters</dd>


<dt>qVec</dt>
<dd>the vector of the number of factors in each clusters</dd>


<dt>ZOneDim</dt>
<dd>ZOneDim</dd>


<dt>constraint</dt>
<dd>constraint</dd>

</dl>

generatePriorThetaY: PriorThetaY list

Description

Usage

Arguments