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
      Cybernetics

Distribution Statement : APPROVED FOR PUBLIC RELEASE