MixingDistribution

The constructor function for a mixing distribution object.
Use this function to prepare an object for use with the appropriate distribution functions.

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 

MixingDistribution function

<dl><dt>distribution</dt>
<dd>The name of the distribution mixture</dd>
<dt>priorParameters</dt>
<dd>The prior parameters</dd>
<dt>conjugate</dt>
<dd>Whether the prior is conjugate to the Likelihood.</dd>
<dt>mhStepSize</dt>
<dd>The scale of the proposal parameter for the Metropolis Hastings algorithm. Not needed for conjugate mixtures.</dd>
<dt>hyperPriorParameters</dt>
<dd>Vector of hyperPriorParameters for the distribution.</dd></dl>

Arguments

Create a mixing distribution object — MixingDistribution

<dl>

<dt>distribution</dt>
<dd>The name of the distribution mixture</dd>


<dt>priorParameters</dt>
<dd>The prior parameters</dd>


<dt>conjugate</dt>
<dd>Whether the prior is conjugate to the Likelihood.</dd>


<dt>mhStepSize</dt>
<dd>The scale of the proposal parameter for the Metropolis Hastings algorithm. Not needed for conjugate mixtures.</dd>


<dt>hyperPriorParameters</dt>
<dd>Vector of hyperPriorParameters for the distribution.</dd>

</dl>

MixingDistribution: Create a mixing distribution object

Description

Usage

Arguments