DirichletProcessMvnormal2

Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior.

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 

DirichletProcessMvnormal2 function

<dl><dt>y</dt>
<dd>Data</dd>
<dt>g0Priors</dt>
<dd>Prior parameters for the base distribution.</dd>
<dt>alphaPriors</dt>
<dd>Alpha prior parameters. See <code>UpdateAlpha</code>.</dd></dl>

Arguments

Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior. — DirichletProcessMvnormal2

<dl>

<dt>y</dt>
<dd>Data</dd>


<dt>g0Priors</dt>
<dd>Prior parameters for the base distribution.</dd>


<dt>alphaPriors</dt>
<dd>Alpha prior parameters. See <code>UpdateAlpha</code>.</dd>

</dl>

DirichletProcessMvnormal2: Create a Dirichlet mixture of multivariate normal distributions with semi-conjugate prior.

Description

Usage

Arguments