LikelihoodFunction

Collecting the fitted cluster parameters and number of datapoints associated with each parameter a likelihood can be calculated.
Each cluster is weighted by the number of datapoints assigned.

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 

LikelihoodFunction function

<dl><dt>dpobj</dt>
<dd>Dirichlet process object.</dd>
<dt>ind</dt>
<dd>The iteration number. Defaults to the last iteration.</dd></dl>

Arguments

The Likelihood function of a Dirichlet process object. — LikelihoodFunction

<dl>

<dt>dpobj</dt>
<dd>Dirichlet process object.</dd>


<dt>ind</dt>
<dd>The iteration number. Defaults to the last iteration.</dd>

</dl>

LikelihoodFunction: The Likelihood function of a Dirichlet process object.

Description

Usage

Value

Arguments

Examples