collapsedGibbsBinMix {BayesBinMix}

collapsed Gibbs sampler

Description

This function applied collapsed Gibbs sampling assuming that the number of mixture components is known.

Usage

collapsedGibbsBinMix(alpha, beta, gamma, K, m, burn, 
	data, thinning, z.true, outputDir)

Arguments

alpha

First shape parameter of the Beta prior distribution (strictly positive). Defaults to 1.

beta

Second shape parameter of the Beta prior distribution (strictly positive). Defaults to 1.

gamma

K-dimensional vector (positive) corresponding to the parameters of the Dirichlet prior of the mixture weights. Default value: rep(1,K).

K

Number of clusters.

m

Number of MCMC iterations.

burn

The number of initial MCMC iterations that will be discarded as burn-in period.

data

Binary data array.

thinning

Integer that defines a thinning of the reported MCMC sample. Under the default setting, every 5th MCMC iteration is saved.

z.true

An optional vector of cluster assignments considered as the ground-truth clustering of the observations. Useful for simulations.

outputDir

The name of the produced output folder.

Note

Not really used.

Author(s)

Panagiotis Papastamoulis