PosteriorClusters

Using the stick breaking representation the user can draw the posterior clusters and weights for a fitted Dirichlet Process.
See also <code>PosteriorFunction</code>.

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 

PosteriorClusters function

<dl><dt>dpobj</dt>
<dd>Fitted Dirichlet process</dd>
<dt>ind</dt>
<dd>Index for which the posterior will be drawn from. Defaults to the last iteration of the fit.</dd></dl>

Arguments

Generate the posterior clusters of a Dirichlet Process — PosteriorClusters

<dl>

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


<dt>ind</dt>
<dd>Index for which the posterior will be drawn from. Defaults to the last iteration of the fit.</dd>

</dl>

PosteriorClusters: Generate the posterior clusters of a Dirichlet Process

Description

Usage

Value

Arguments

Examples