Initialise

dpObj

TRUE/FALSE value for whether the cluster parameters should be from the posterior. If false then the values are from the prior.

posterior

Number of auxiliary variables to use for a non-conjugate mixing distribution. Defaults to m=3. See <code><a rd-options="" href="/link/ClusterComponentUpdate?package=dirichletprocess&version=0.2.0" data-mini-rdoc="dirichletprocess::ClusterComponentUpdate">ClusterComponentUpdate</a></code> for more details on m.

Logical flag indicating whether to output the acceptance ratio for non-conjugate mixtures.

verbose

Initialise a Dirichlet process object by assigning all the data points to a single cluster with a posterior or prior draw for parameters.

Create Dirichlet process objects that can be used as infinite mixture models in a variety of ways.
Some examples include; density estimation, Poisson process intensity inference, hierarchical modelling and clustering.
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

Initialise function

Number of auxiliary variables to use for a non-conjugate mixing distribution. Defaults to m=3. See <code><a rd-options='' href='ClusterComponentUpdate'>ClusterComponentUpdate</a></code> for more details on m.

Initialise: Initialise a Dirichlet process object

Description

Usage

Arguments

Value