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
estimated posterior with ncomp components. Structured in a list object as follows:
alphahyperparameters influencing the active components in the posterior.
betahyperparameters regarding shaping of the Normal-Wishart posteriors.
nuhyperparameters regarding shaping of the Normal-Wishart posteriors.
meanhyperparameters regarding shaping of the Normal-Wishart posteriors.
wishhyperparameters regarding shaping of the Normal-Wishart posteriors.
References
Bishop, C. M. (2006) _Pattern Recognition and Machine Learning_, Chapter 10, Springer.