Variational Bayesian Inference for Hidden Markov Models With Multivariate Gaussian Output Distributions
This work addresses the training of HMMs for time series analysis or pattern recognition tasks, but it appears incremental as it combines existing techniques without claiming major breakthroughs.
The authors tackled the problem of training Hidden Markov Models with multivariate Gaussian output distributions by applying Variational Bayesian Inference, a second-order training technique, and are currently evaluating it through case studies and comparisons to related approaches.
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.