Accession Number : ADA307097
Title : Factorial Hidden Markov Models.
Descriptive Note : Memorandum rept.,
Corporate Author : MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
Personal Author(s) : Ghahramani, Zoubin ; Jordan, Michael I.
PDF Url : ADA307097
Report Date : JAN 1996
Pagination or Media Count : 9
Abstract : We present a framework for learning in hidden Markov models with distributed state representations. Within this framework, we derive a learning algorithm based on the Expectation-Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved analytically. However, due to the combinatorial nature of the hidden state representation, the exact E-step is intractable. A simple and tractable mean field approximation is derived. Empirical results on a set of problems suggest that both the mean field approximation and Gibbs sampling are viable alternatives to the computationally expensive exact algorithm.
Descriptors : *MATHEMATICAL MODELS, *MARKOV PROCESSES, ALGORITHMS, DISTRIBUTION, MAXIMUM LIKELIHOOD ESTIMATION, SAMPLING, MEAN, LEARNING, FACTORIAL DESIGN.
Subject Categories : Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE