true_cluster_labels

Identifies the correct clusters labels, in any dimension,
when cluster parameters and global parameters are matched.

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 

true_cluster_labels function

<dl><dt>array</dt>
<dd>The array containing matching indexes.</dd>
<dt>dpObj</dt>
<dd>A hierarchical dirichletprocess object.</dd></dl>

Arguments

Identifies the correct clusters labels, in any dimension,
when cluster parameters and global parameters are matched. — true_cluster_labels

<dl>

<dt>array</dt>
<dd>The array containing matching indexes.</dd>


<dt>dpObj</dt>
<dd>A hierarchical dirichletprocess object.</dd>

</dl>

true_cluster_labels: Identifies the correct clusters labels, in any dimension, when cluster parameters and global parameters are matched.

Description

Usage

Value

Arguments