WeibullMixtureCreate

See <code>DirichletProcessWeibull</code> for the default prior and hyper prior distributions.

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 

WeibullMixtureCreate function

<dl><dt>priorParameters</dt>
<dd>Prior parameters for the Weibull parameters</dd>
<dt>mhStepSize</dt>
<dd>Metropolis Hastings Step Size</dd>
<dt>hyperPriorParameters</dt>
<dd>Parameters for the hyper-priors</dd></dl>

Arguments

Create a Weibull mixing distribution. — WeibullMixtureCreate

<dl>

<dt>priorParameters</dt>
<dd>Prior parameters for the Weibull parameters</dd>


<dt>mhStepSize</dt>
<dd>Metropolis Hastings Step Size</dd>


<dt>hyperPriorParameters</dt>
<dd>Parameters for the hyper-priors</dd>

</dl>

WeibullMixtureCreate: Create a Weibull mixing distribution.

Description

Usage

Value

Arguments