ChangeObservations

Using a fitted Dirichlet process object include new data. The new data will be assigned to the best fitting cluster for each point.

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 

ChangeObservations function

<dl><dt>dpobj</dt>
<dd>The Dirichlet process object.</dd>
<dt>newData</dt>
<dd>New data to be included</dd></dl>

Arguments

Change the observations of fitted Dirichlet Process. — ChangeObservations

<dl>

<dt>dpobj</dt>
<dd>The Dirichlet process object.</dd>


<dt>newData</dt>
<dd>New data to be included</dd>

</dl>

ChangeObservations: Change the observations of fitted Dirichlet Process.

Description

Usage

Value

Arguments

Examples