CalculateProposalLambda

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

CalculateProposalLambda function

<dl><dt>hparam</dt>
<dd>hparam</dd>
<dt>thetaYList</dt>
<dd>thetaYList</dd>
<dt>CxyList</dt>
<dd>CxyList</dd>
<dt>constraint</dt>
<dd>constraint</dd>
<dt>m</dt>
<dd>the number of clusters</dd>
<dt>p</dt>
<dd>the number of features</dd>
<dt>qVec</dt>
<dd>the vector of the number of factors in each clusters</dd></dl>

Arguments

CalculateProposalLambda — CalculateProposalLambda

<dl>

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


<dt>thetaYList</dt>
<dd>thetaYList</dd>


<dt>CxyList</dt>
<dd>CxyList</dd>


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


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


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


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

</dl>

CalculateProposalLambda: CalculateProposalLambda

Description

Usage

Arguments