WeibullMixtureCreate

Prior parameters for the Weibull parameters

priorParameters

mhStepSize

hyperPriorParameters

See <code><a rd-options="" href="/link/DirichletProcessWeibull?package=dirichletprocess&version=0.2.1" data-mini-rdoc="dirichletprocess::DirichletProcessWeibull">DirichletProcessWeibull</a></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

WeibullMixtureCreate function

See <code><a rd-options='' href='DirichletProcessWeibull'>DirichletProcessWeibull</a></code> for the default prior and hyper prior distributions.

WeibullMixtureCreate: Create a Weibull mixing distribution.

Description

Usage

Arguments

Value