print.dirichletprocess

Print a Dirichlet process object. This will print some basic information
about the <code>dirichletprocess</code> object.

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 

print.dirichletprocess function

<dl><dt>x</dt>
<dd>Dirichlet Process Object to print.</dd>
<dt>param_summary</dt>
<dd>If <code>TRUE</code>, print the overall averages of each
parameter of the model. Note that this averages over all clusters and over
all iterations, so it will only give a loose sense of the resulting DPM
model.</dd>
<dt>digits</dt>
<dd>Integer; Number of digits to display.</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

Print the Dirichlet process object — print.dirichletprocess

<dl>

<dt>x</dt>
<dd>Dirichlet Process Object to print.</dd>


<dt>param_summary</dt>
<dd>If <code>TRUE</code>, print the overall averages of each
parameter of the model. Note that this averages over all clusters and over
all iterations, so it will only give a loose sense of the resulting DPM
model.</dd>


<dt>digits</dt>
<dd>Integer; Number of digits to display.</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

print.dirichletprocess: Print the Dirichlet process object

Description

Usage

Arguments

Examples