burn.DPMMclust

Utility function for burning MCMC iteration from a <code>DPMMclust</code> object.

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
modeling oriented towards flow-cytometry data preprocessing applications. Method is
detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

Boris P. Hejblum

NPflow

Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry
Data

Chariff Alkhassim

Francois Caron

burn.DPMMclust function

<dl><dt>x</dt>
<dd>a <code>DPMMclust</code> object.</dd>
<dt>burnin</dt>
<dd>the number of MCMC iterations to burn (default is <code>0</code>).</dd>
<dt>thin</dt>
<dd>the spacing at which MCMC iterations are kept.
Default is <code>1</code>, i.e. no thining.</dd></dl>

Arguments

Author

Burning MCMC iterations from a Dirichlet Process Mixture Model. — burn.DPMMclust

<dl>

<dt>x</dt>
<dd>a <code>DPMMclust</code> object.</dd>


<dt>burnin</dt>
<dd>the number of MCMC iterations to burn (default is <code>0</code>).</dd>


<dt>thin</dt>
<dd>the spacing at which MCMC iterations are kept.
Default is <code>1</code>, i.e. no thining.</dd>

</dl>

burn.DPMMclust: Burning MCMC iterations from a Dirichlet Process Mixture Model.

Description

Usage

Value

Arguments

Author

See Also