Mixing probability for creating new cluster

A Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" <doi:10.48550/arXiv.2305.17631>.

Ana Carolina da Cruz

BayesCPclust

A Bayesian Approach for Clustering Constant-Wise Change-Point
Data

qn0 function

<dl><dt>alpha0</dt>
<dd>A scalar defining the parameter for the Dirichlet process prior
that controls the number of clusters (or its initial values)</dd>
<dt>w</dt>
<dd>A scalar representing the minimum number of points in each interval between two change points</dd>
<dt>N</dt>
<dd>A scalar representing the number of data sequences</dd>
<dt>M</dt>
<dd>A scalar representing the number of points available for each data sequence</dd>
<dt>bs</dt>
<dd>The hyperparameter value for the scale parameter in the inverse-gamma prior for the variance
component</dd>
<dt>as</dt>
<dd>The hyperparameter value for the shape parameter in the inverse-gamma prior for the variance
component</dd>
<dt>kstar</dt>
<dd>A scalar with the number maximum of change points in all clusters</dd>
<dt>lambda</dt>
<dd>A scalar defining the parameter for the Truncate Poisson distribution
that controls the number of change points (or its initial values)</dd>
<dt>Yn</dt>
<dd>A vector or matrix with data sequences for a cluster</dd></dl>

Arguments

Mixing probability for creating new cluster — qn0

<dl>

<dt>alpha0</dt>
<dd>A scalar defining the parameter for the Dirichlet process prior
that controls the number of clusters (or its initial values)</dd>


<dt>w</dt>
<dd>A scalar representing the minimum number of points in each interval between two change points</dd>


<dt>N</dt>
<dd>A scalar representing the number of data sequences</dd>


<dt>M</dt>
<dd>A scalar representing the number of points available for each data sequence</dd>


<dt>bs</dt>
<dd>The hyperparameter value for the scale parameter in the inverse-gamma prior for the variance
component</dd>


<dt>as</dt>
<dd>The hyperparameter value for the shape parameter in the inverse-gamma prior for the variance
component</dd>


<dt>kstar</dt>
<dd>A scalar with the number maximum of change points in all clusters</dd>


<dt>lambda</dt>
<dd>A scalar defining the parameter for the Truncate Poisson distribution
that controls the number of change points (or its initial values)</dd>


<dt>Yn</dt>
<dd>A vector or matrix with data sequences for a cluster</dd>

</dl>

qn0: Mixing probability for creating new cluster

Description

Usage

Value

Arguments

See Also

Examples