varbayes: varbayes

In VBmix: Variational Bayesian Mixture Models

Description

estimates the variational posterior distribution of a GMM on data using the variational EM algorithm (see references). A lower bound is calculated and monitored at each iteration. This posterior can be used for various purposes (e.g. MC proposal distribution). It can be transformed using extractSimpleModel, outputing a GMM.

Usage

varbayes(data, ncomp, thres = 0.1, maxit = NULL)

Arguments

data	matrix of row-elements.
ncomp	number of components in the posterior.
thres	threshold for lower bound variations between 2 iterations. Convergence is decided if this variation is below thres.
maxit	if NULL, the stopping criterion is related to thres. If not NULL, maxit iterations are performed.

Value

A list object, with the following items:

model	posterior variational distribution.
data	a copy of the data parameter.
nk	counts, for each iteration, of the population modeled by each Gaussian component.
agitation	agitation measures (see Beal 2003 for explanation) for each iteration and Gaussian component.
bound	latest monitored bound value (convergence criterion maximized throughout the process).

The model item is structured in a list as follows:

alpha	hyperparameters influencing the active components in the posterior.
beta	hyperparameters regarding shaping of the Normal-Wishart posteriors.
nu	hyperparameters regarding shaping of the Normal-Wishart posteriors.
mean	hyperparameters regarding shaping of the Normal-Wishart posteriors.
wish	hyperparameters regarding shaping of the Normal-Wishart posteriors.

Author(s)

Pierrick Bruneau

References

Bishop, C. M. (2006) Pattern Recognition and Machine Learning, Chapter 10, Springer.

Beal, M. J. (2003) Variational Algorithms for approximate inference, PhD thesis, University of London.

See Also

EM extractSimpleModel

Examples

temp <- varbayes(irisdata, 20)

VBmix documentation built on May 30, 2017, 2:34 a.m.