DirichletHMMCreate

Create a hidden Markov model where the data is believed to be generated from the mixing object distribution.

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 

DirichletHMMCreate function

<dl><dt>x</dt>
<dd>Data to be modelled</dd>
<dt>mdobj</dt>
<dd>Mixing disitribution object</dd>
<dt>alpha</dt>
<dd>Alpha parameter</dd>
<dt>beta</dt>
<dd>Beta parameter</dd></dl>

Arguments

Create a generic Dirichlet process hidden Markov Model — DirichletHMMCreate

<dl>

<dt>x</dt>
<dd>Data to be modelled</dd>


<dt>mdobj</dt>
<dd>Mixing disitribution object</dd>


<dt>alpha</dt>
<dd>Alpha parameter</dd>


<dt>beta</dt>
<dd>Beta parameter</dd>

</dl>

DirichletHMMCreate: Create a generic Dirichlet process hidden Markov Model

Description

Usage

Arguments