language: en

MASSACHUSETTS INSTITUTE OF TECHNOLOGY ARTIFICIAL INTELLIGENCE LABORATORY and CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING DEPARTMENT OF BRAIN AND COGNITIVE SCIENCES
A.I. Memo No. 1565 February 2, 1996 C.B.C.L. Memo No. 132

Probabilistic Independence Networks for Hidden Markov Probability Models

Padhraic Smyth, David Heckerman, and Michael Jordan

Abstract Graphical techniques for modeling the dependencies of random variables have been explored in a variety of different areas including statistics, statistical physics, artificial intelligence, speech recognition, image processing, and genetics. Formalisms for manipulating these models have been developed relatively independently in these research communities. In this paper we explore hidden Markov models (HMMs) and related structures within the general framework of probabilistic independence networks (PINs). The paper contains a self-contained review of the basic principles of PINs. It is shown that the well-known forward-backward (F-B) and Viterbi algorithms for HMMs are special cases of more general inference algorithms for arbitrary PINs. Furthermore, the existence of inference and estimation algorithms for more general graphical models provides a set of analysis tools for HMM practitioners who wish to explore a richer class of HMM structures. Examples of relatively complex models to handle sensor fusion and coarticulation in speech recognition are introduced and treated within the graphical model framework to illustrate the advantages of the general approach.

[Copyright c Massachusetts Institute of Technology, 1996 This report describes research done at the Department of Information and Computer Science, University of California, Irvine, the Jet Propulsion Laboratory, California Institute of Technology, Microsoft Research, the Center for Biological and Computational Learning, and the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. The authors can be contacted as pjs@aig.jpl.nasa.gov, heckerma@microsoft.com, and jordan@psyche.mit.edu.]

[Support for CBCL is provided in part by a grant from the NSF (ASC-9217041). Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Dept. of Defense. MIJ gratefully acknowledges discussions with Steffen Lauritzen on the application of the IPF algorithm to UPINs.]