Likelihood.beta

Likelihood.beta2

Likelihood.exponential

Likelihood

Likelihood.mvnormal

Likelihood.mvnormal2

Likelihood.normalFixedVariance

Likelihood.normal

Evaluate the Likelihood of some data \(x\) for some parameter \(\theta\).

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 

Likelihood.beta function

<dl><dt>mdObj</dt>
<dd>Mixing Distribution</dd>
<dt>x</dt>
<dd>Data</dd>
<dt>theta</dt>
<dd>Parameters of distribution</dd></dl>

Arguments

Mixing Distribution Likelihood — Likelihood.beta

<dl>

<dt>mdObj</dt>
<dd>Mixing Distribution</dd>


<dt>x</dt>
<dd>Data</dd>


<dt>theta</dt>
<dd>Parameters of distribution</dd>

</dl>

Mixing Distribution Likelihood

Likelihood.beta: Mixing Distribution Likelihood

Description

Usage

Value

Arguments