  Hidden Markov Models (HMM) have been used for several years in many time
series analysis or pattern recognitions tasks. HMM are often trained by means
of the Baum-Welch algorithm which can be seen as a special variant of an
expectation maximization (EM) algorithm. Second-order training techniques such
as Variational Bayesian Inference (VI) for probabilistic models regard the
parameters of the probabilistic models as random variables and define
distributions over these distribution parameters, hence the name of this
technique. VI can also bee regarded as a special case of an EM algorithm. In
this article, we bring both together and train HMM with multivariate Gaussian
output distributions with VI. The article defines the new training technique
for HMM. An evaluation based on some case studies and a comparison to related
approaches is part of our ongoing work.
