Probabilistic Independence Networks for Hidden Markov Probability Models
- 1 February 1997
- journal article
- Published by MIT Press in Neural Computation
- Vol. 9 (2) , 227-269
- https://doi.org/10.1162/neco.1997.9.2.227
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 presents 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.Keywords
This publication has 26 references indexed in Scilit:
- Learning Bayesian networks: The combination of knowledge and statistical dataMachine Learning, 1995
- On the effective implementation of the iterative proportional fitting procedureComputational Statistics & Data Analysis, 1995
- Operations for Learning with Graphical ModelsJournal of Artificial Intelligence Research, 1994
- Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chainsIEEE Transactions on Speech and Audio Processing, 1994
- Applications of a general propagation algorithm for probabilistic expert systemsStatistics and Computing, 1992
- Independence properties of directed markov fieldsNetworks, 1990
- Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of ImagesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1984
- An Introduction to Spatial Point Processes and Markov Random FieldsInternational Statistical Review, 1981
- Coarticulation in recent speech production modelsJournal of Phonetics, 1977
- Statistical Inference for Probabilistic Functions of Finite State Markov ChainsThe Annals of Mathematical Statistics, 1966