HierarchicalMvnormal2Create

Create a Mixing Object for a hierarchical semi-conjugate
Multivariate Normal Dirichlet process object.

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 

HierarchicalMvnormal2Create function

<dl><dt>n</dt>
<dd>Number of data sets</dd>
<dt>priorParameters</dt>
<dd>The prior parameters for the top level base distribution.</dd>
<dt>alphaPrior</dt>
<dd>Individual level concentration parameter priors.</dd>
<dt>gammaPrior</dt>
<dd>Concentration parameter for the top level priors.</dd>
<dt>num_sticks</dt>
<dd>Number of stick breaking values to use.</dd></dl>

Arguments

Create a Mixing Object for a hierarchical semi-conjugate
Multivariate Normal Dirichlet process object. — HierarchicalMvnormal2Create

<dl>

<dt>n</dt>
<dd>Number of data sets</dd>


<dt>priorParameters</dt>
<dd>The prior parameters for the top level base distribution.</dd>


<dt>alphaPrior</dt>
<dd>Individual level concentration parameter priors.</dd>


<dt>gammaPrior</dt>
<dd>Concentration parameter for the top level priors.</dd>


<dt>num_sticks</dt>
<dd>Number of stick breaking values to use.</dd>

</dl>

HierarchicalMvnormal2Create: Create a Mixing Object for a hierarchical semi-conjugate Multivariate Normal Dirichlet process object.

Description

Usage

Value

Arguments