Hidden Markov Model with Duration Side Information for Novel HMMD Derivation, with Application to Eukaryotic Gene Finding. (4th October 2010) - Winters-Hilt Winters-Hilt, S. S.; Jiang Jiang, Z. Z.; Baribault Baribault, C. C.
Abstract : We describe a new method to introduce duration into an HMM using side information that can be put in the form of a martingale series. Our method makes use of ratios of duration cumulant probabilities in a manner that meshes with the column-level dynamic programming construction. Other information that could be incorporated, via ratios of sequence matches, includes an EST and homology information. A familiar occurrence of a martingale in HMM-based efforts is the sequence-likelihood ratio classification. Our method suggests a general procedure for piggybacking other side information as ratios of side information probabilities, in association (e.g., one-to-one) with the duration-probability ratios. Using our method, the HMM can be fully informed by the side information available during its dynamic table optimization—in Viterbi path calculations in particular.