ClusterParameterUpdate

Update the parameters of each individual cluster using all the data assigned to the particular cluster.
A sample is taken from the posterior distribution using a direct sample if the mixing distribution is conjugate or the Metropolis Hastings algorithm for non-conjugate mixtures.

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 

ClusterParameterUpdate function

<dl><dt>dpObj</dt>
<dd>Dirichlet process object</dd></dl>

Arguments

Update the cluster parameters of the Dirichlet process. — ClusterParameterUpdate

<dl>

<dt>dpObj</dt>
<dd>Dirichlet process object</dd>

</dl>

ClusterParameterUpdate: Update the cluster parameters of the Dirichlet process.

Description

Usage

Value

Arguments

Examples