DirichletProcessCreate

Using a previously created Mixing Distribution Object (<code>mdObject</code>) create a Dirichlet process object.
`<code>alphaPriorParameters</code> sets the parameters for alpha using the shape-rate specification of the gamma distribution.

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 

DirichletProcessCreate function

<dl><dt>x</dt>
<dd>Data</dd>
<dt>mdObject</dt>
<dd>Mixing Distribution Object</dd>
<dt>alphaPriorParameters</dt>
<dd>Prior parameters for the concentration parameter of the Dirichlet Process</dd>
<dt>mhDraws</dt>
<dd>Number of posterior samples to take in the nonconjugate case</dd></dl>

Arguments

Create a Dirichlet Process object — DirichletProcessCreate

<dl>

<dt>x</dt>
<dd>Data</dd>


<dt>mdObject</dt>
<dd>Mixing Distribution Object</dd>


<dt>alphaPriorParameters</dt>
<dd>Prior parameters for the concentration parameter of the Dirichlet Process</dd>


<dt>mhDraws</dt>
<dd>Number of posterior samples to take in the nonconjugate case</dd>

</dl>

DirichletProcessCreate: Create a Dirichlet Process object

Description

Usage

Arguments