ClusterLabelPredict

Given a fitted Dirichlet process object and some new data use this function to predict what clusters the new data belong to and associated cluster parameters.

Perform nonparametric Bayesian analysis using Dirichlet
processes without the need to program the inference algorithms.
Utilise included pre-built models or specify custom
models and allow the 'dirichletprocess' package to handle the
Markov chain Monte Carlo sampling.
Our Dirichlet process objects can act as building blocks for a variety
of statistical models including and not limited to: density estimation,
clustering and prior distributions in hierarchical models.
See Teh, Y. W. (2011)
<https://www.stats.ox.ac.uk/~teh/research/npbayes/Teh2010a.pdf>,
among many other sources.

Dean Markwick

dirichletprocess

Build Dirichlet Process Objects for Bayesian Modelling

Gordon J. Ross

Kees Mulder

Giovanni Sighinolfi

Filippo Fiocchi 

ClusterLabelPredict function

<dl><dt>dpobj</dt>
<dd>Fitted Dirichlet Process</dd>
<dt>newData</dt>
<dd>New data to have cluster labels predicted.</dd></dl>

Arguments

Predict the cluster labels of some new data. — ClusterLabelPredict

<dl>

<dt>dpobj</dt>
<dd>Fitted Dirichlet Process</dd>


<dt>newData</dt>
<dd>New data to have cluster labels predicted.</dd>

</dl>

ClusterLabelPredict: Predict the cluster labels of some new data.

Description

Usage

Value

Arguments

Examples