The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Probabilistic Independence Networks for Hidden Markov Probability Models
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.
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Cites
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Cited by
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
Self-citation(2003) · Cited by 1,680
Wavelet-based statistical signal processing using hidden Markov models
Crouse, M. S., Nowak, R. D., & Baraniuk, R. G. (1998) · IEEE Transactions on Signal Processing · doi:10.1109/78.668544 · Cited by 1,643
Factorial Hidden Markov Models
Ghahramani, Z., & Jordan, M. I. (1997) · Machine Learning · doi:10.1023/a:1007425814087 · Cited by 1,157
Self-citationLearning dynamic Bayesian networks
Ghahramani, Z. (1998) · Lecture Notes in Computer Science · doi:10.1007/bfb0053999 · Cited by 543
Metadata
- Authors
- Smyth, P., Heckerman, D., & Jordan, M. I.
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 1997-02-01
- Volume
- 9
- Issue
- 2
- Pages
- 227-269
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2147496287
- Cited by
- 257
- References
- 50
- FWCI
- 22.82
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
