LikelihoodDP

Calculate the likelihood of each data point with its parameter.

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 

LikelihoodDP function

<dl><dt>dpobj</dt>
<dd>The dirichletprocess object on which to calculate the likelihood.</dd></dl>

Arguments

The likelihood of the Dirichlet process object — LikelihoodDP

<dl>

<dt>dpobj</dt>
<dd>The dirichletprocess object on which to calculate the likelihood.</dd>

</dl>

LikelihoodDP: The likelihood of the Dirichlet process object

Description

Usage

Arguments