gibbsBinMix: Standard Gibbs sampler

In BayesBinMix: Bayesian Estimation of Mixtures of Multivariate Bernoulli Distributions

Description

This function implements full Gibbs sampling to simulate an MCMC sample from the posterior distribution assuming known number of mixture components.

Usage

gibbsBinMix(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	Kmax-dimensional vector (positive) corresponding to the parameters of the Dirichlet prior of the mixture weights. Default value: rep(1,Kmax).
K	Number of clusters.
m	Number of MCMC iterations.
burn	Burn-in period.
data	Binary data.
thinning	Thinning of the simulated chain.
z.true	An optional vector of cluster assignments considered as the ground-truth clustering of the observations. Useful for simulations.
outputDir	Output directory.

Details

Not really used.

Author(s)

Panagiotis Papastamoulis

BayesBinMix documentation built on May 2, 2019, 3:26 a.m.