We present a variational Bayesian framework for performing inference, density estimation and model selection in a special class of graphical models--Hidden Markov Random Fields (H...
Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang
Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hi...
Background: Hidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) ...
Background: Profile HMMs (hidden Markov models) provide effective methods for modeling the conserved regions of protein families. A limitation of the resulting domain models is th...