BetaMixtureCreate

See <code>DirichletProcessBeta</code> for the default prior and hyper prior distributions.

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 

BetaMixtureCreate function

<dl><dt>priorParameters</dt>
<dd>The prior parameters for the base measure.</dd>
<dt>mhStepSize</dt>
<dd>The Metropolis Hastings step size. A numeric vector of length 2.</dd>
<dt>maxT</dt>
<dd>The upper bound of the Beta distribution. Defaults to 1 for the standard Beta distribution.</dd>
<dt>hyperPriorParameters</dt>
<dd>The parameters for the hyper prior.</dd></dl>

Arguments

Create a Beta mixing distribution. — BetaMixtureCreate

<dl>

<dt>priorParameters</dt>
<dd>The prior parameters for the base measure.</dd>


<dt>mhStepSize</dt>
<dd>The Metropolis Hastings step size. A numeric vector of length 2.</dd>


<dt>maxT</dt>
<dd>The upper bound of the Beta distribution. Defaults to 1 for the standard Beta distribution.</dd>


<dt>hyperPriorParameters</dt>
<dd>The parameters for the hyper prior.</dd>

</dl>

BetaMixtureCreate: Create a Beta mixing distribution.

Description

Usage

Value

Arguments