GaussianMixtureCreate

See <code>DirichletProcessGaussian</code> for details on the base measure.

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 

GaussianMixtureCreate function

<dl><dt>priorParameters</dt>
<dd>Prior parameters for the base measure.</dd></dl>

Arguments

Create a Normal mixing distribution — GaussianMixtureCreate

<dl>

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

</dl>

GaussianMixtureCreate: Create a Normal mixing distribution

Description

Usage

Value

Arguments