Using Neal's algorithm 4 or 8 depending on conjugacy the sampling procedure for a Dirichlet process is carried out.
Lists of both cluster parameters, weights and the sampled concentration values are included in the fitted <code>dpObj</code>.
When <code>update_prior</code> is set to <code>TRUE</code> the parameters of the base measure are also updated.

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 

Fit function

<dl><dt>dpObj</dt>
<dd>Initialised Dirichlet Process object</dd>
<dt>its</dt>
<dd>Number of iterations to use</dd>
<dt>updatePrior</dt>
<dd>Logical flag, defaults to <code>FAlSE</code>. Set whether the parameters of the base measure are updated.</dd>
<dt>progressBar</dt>
<dd>Logical flag indicating whether to display a progress bar.</dd></dl>

Arguments

Fit the Dirichlet process object — Fit

<dl>

<dt>dpObj</dt>
<dd>Initialised Dirichlet Process object</dd>


<dt>its</dt>
<dd>Number of iterations to use</dd>


<dt>updatePrior</dt>
<dd>Logical flag, defaults to <code>FAlSE</code>. Set whether the parameters of the base measure are updated.</dd>


<dt>progressBar</dt>
<dd>Logical flag indicating whether to display a progress bar.</dd>

</dl>

Fit: Fit the Dirichlet process object

Description

Usage

Value

Arguments

References