createMixtureTarget: Mixture target distribution

In PAWL: Implementation of the PAWL algorithm

Description

Create the posterior distribution of the parameters of a mixture of univariate gaussian distributions, with a fixed (known) number of components.

Usage

    createMixtureTarget(mixturesample, mixturesize, ncomponents, mixtureparameters)

Arguments

All the arguments are optional, since if none is given, a mixture distribution with 4 components will be created, as in Jasra, Holmes, Stephens, "MCMC and label switching problem in Bayesian mixture models", published in Statistical Science (2005).

mixturesample	Object of class "vector": data set to be used. If not provided, a synthetic data set is generated.
mixturesize	Object of class "numeric": represents the data set size if a data set is to be generated.
ncomponents	Object of class "numeric": represents the fixed number of components to be used.
mixtureparameters	Object of class "list": provides the parameters to be used if a data set has to be generated. The parameters include the number of components, the component weights, means and variances.

Value

The function returns an object of class target-class, with a name, a dimension, a function giving the log density, a function to generate sample from the distribution, parameters of the distribution, and a function to draw init points for the MCMC algorithms. The log density involves a likelihood and a prior, and the prior is as in Richardson and Green, "On Bayesian analysis of mixtures with an unknown number of components", published in JRSS B, 1997.

Author(s)

Luke Bornn <[e-mail]>, Pierre E. Jacob <[e-mail]>

References

Jasra, Holmes, Stephens, "MCMC and label switching problem in Bayesian mixture models", published in Statistical Science (2005). Richardson and Green, "On Bayesian analysis of mixtures with an unknown number of components", published in JRSS B, 1997.

See Also

target-class, createTrimodalTarget

PAWL documentation built on May 2, 2019, 5:58 a.m.