Predictive.exponential

Predictive

Predictive.mvnormal

Predictive.normalFixedVariance

Predictive.normal

Calculate how well the prior predicts the data.

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 

Predictive.exponential function

<dl><dt>mdObj</dt>
<dd>The distribution</dd>
<dt>x</dt>
<dd>The data</dd></dl>

Arguments

Calculate how well the prior predicts the data. — Predictive.exponential

<dl>

<dt>mdObj</dt>
<dd>The distribution</dd>


<dt>x</dt>
<dd>The data</dd>

</dl>

Calculate how well the prior predicts the data.

Predictive.exponential: Calculate how well the prior predicts the data.

Description

Usage

Value

Arguments