Cited by 1,157Bayesian Modeling and Causal InferenceBayesian Methods and Mixture ModelsSpeech Recognition and Synthesis

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Factorial Hidden Markov Models

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Ghahramani, Zoubin, Jordan, Michael I.·1997·Machine Learning

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Research works printed but not linked: 35

  1. Baum, L., Petrie, T., Soules, G., & Weiss, N. (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. The Annals of Mathematical Statistics, 41, 164–171. · DOI
  2. Bengio, Y., & Frasconi, P. (1995). An input-output HMM architecture. In G. Tesauro, D. S. Touretzky, & T. K. Leen (Eds.), Advances in neural information processing systems 7, pp. 427–434. Cambridge, MA: MIT Press.
  3. Cacciatore, T. W., & Nowlan, S. J. (1994). Mixtures of controllers for jump linear and non-linear plants. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems 6, pp. 719–726. San Francisco, CA: Morgan Kaufmann.
  4. Conklin, D., & Witten, I. H. (1995). Multiple viewpoint systems for music prediction. Journal of New Music Research, 24, 51–73. · DOI
  5. Cover, T., & Thomas, J. (1991). Elements of information theory. New York: John Wiley.
  6. Dawid, A. P. (1992). Applications of a general propagation algorithm for probabilistic expert systems. Statistics and Computing, 2, 25–36. · DOI
  7. Dean, T., & Kanazawa, K. (1989). A model for reasoning about persistence and causation. Computational Intelligence, 5, 142–150. · DOI · Paper page
  8. Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721–741. · DOI · Paper page
  9. Ghahramani, Z. (1995). Factorial learning and the EM algorithm. In G. Tesauro, D. S. Touretzky, & T. K. Leen (Eds.), Advances in neural information processing systems 7, pp. 617–624. Cambridge, MA: MIT Press.
  10. Heckerman, D. (1995.). A tutorial on learning Bayesian networks. (Technical Report MSR-TR–95–06). Redmond, WA: Microsoft Research.
  11. Hinton, G. E., & Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: Explorations in the microstructure of cognition. Volume 1: Foundations. Cambridge, MA: MIT Press.
  12. Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length, and Helmholtz free energy. In J. D. Cowan, G. Tesauro, & J. Alspector (Eds.), Advances in neural information processing systems 6, pp. 3–10. San Francisco, CA: Morgan Kaufmann.
  13. Jensen, F. V., Lauritzen, S. L., & Olesen, K. G. (1990). Bayesian updating in recursive graphical models by local computations. Computational Statistical Quarterly, 4, 269–282.
  14. Jordan, M. I., Ghahramani, Z., & Saul, L. K. (1997). Hidden Markov decision trees. In M. Mozer, M. Jordan, & T. Petsche (Eds.), Advances in neural information processing systems 9. Cambridge, MA: MIT Press.
  15. Jordan, M. I., & Jacobs, R. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181–214. · DOI
  16. Kanazawa, K., Koller, D., & Russell, S. J. (1995). Stochastic simulation algorithms for dynamic probabilistic networks. In P. Besnard,, & S. Hanks (Eds.), Uncertainty in Artificial Intelligence: Proceedings of the Eleventh Conference. (pp. 346–351). San Francisco, CA: Morgan Kaufmann.
  17. Krogh, A., Brown, M., Mian, I. S., Sjölander, K., & Haussler, D. (1994). Hidden Markov models in computational biology: Applications to protein modeling. Journal of Molecular Biology, 235, 1501–1531. · DOI
  18. Lauritzen, S. L., & Spiegelhalter, D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society B, 157–224. · DOI
  19. McCullagh, P., & Nelder, J. (1989). Generalized linear models. London: Chapman & Hall. · DOI · Paper page
  20. Meila, M., & Jordan, M. I. (1996). Learning fine motion by Markov mixtures of experts. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems 8, pp. 1003–1009. Cambridge, MA: MIT Press.
  21. Neal, R. M. (1992). Connectionist learning of belief networks. Artificial Intelligence, 56, 71–113. · DOI
  22. Neal, R. M. (1993). Probabilistic inference using Markov chain Monte Carlo methods (Technical Report CRG-TR–93–1). Toronto, Ontario: University of Toronto, Department of Computer Science.
  23. Neal, R. M.,& Hinton, G. E. (1993). A new view of the EM algorithm that justifies incremental and other variants. Unpublished manuscript, Department of Computer Science, University of Toronto, Ontario.
  24. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Morgan Kaufmann.
  25. Rabiner, L. R., & Juang, B. H. (1986). An Introduction to hidden Markov models. IEEE Acoustics, Speech & Signal Processing Magazine, 3, 4–16.
  26. Saul, L. K., & Jordan, M. I. (1997). Mixed memory Markov models. In D. Madigan, & P. Smyth (Eds.), Proceedings of the 1997 Conference on Artificial Intelligence and Statistics. Ft. Lauderdale, FL.
  27. Saul, L., Jaakkola, T., & Jordan, M. I. (1996). Mean Field Theory for Sigmoid Belief Networks. Journal of Artificial Intelligence Research, 4, 61–76. · DOI
  28. Saul, L., & Jordan, M. I. (1995). Boltzmann chains and hidden Markov models. In G. Tesauro, D. S. Touretzky, & T. K. Leen (Eds.), Advances in neural information processing systems 7, pp. 435–442. Cambridge, MA: MIT Press.
  29. Saul, L., & Jordan, M. I. (1996). Exploiting tractable substructures in Intractable networks. In D. S. Touretzky, M. C. Mozer, & M. E. Hasselmo (Eds.), Advances in neural information processing systems 8, pp. 486–492. Cambridge, MA: MIT Press.
  30. Smyth, P., Heckerman, D., & Jordan, M. I. (1997). Probabilistic independence networks for hidden Markov probability models. Neural Computation, 9, 227–269. · DOI
  31. Stolcke, A., & Omohundro, S. (1993). Hidden Markov model induction by Bayesian model merging. In S.J. Hanson, J. D. Cowan, & C. L. Giles (Eds.), Advances in neural information processing systems 5, pp. 11–18. San Francisco, CA: Morgan Kaufmann.
  32. Tanner, M. A., & Wong, W. H. (1987). The calculation of posterior distributions by data augmentation (with discussion). Journal of the American Statistical Association, 82, 528–550. · DOI
  33. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimal decoding algorithm. IEEE Transactions Information Theory, IT-13, 260–269. · DOI
  34. Williams, C. K. I., & Hinton, G. E. (1991). Mean field networks that learn to discriminate temporally distorted strings. In D. Touretzky, J. Elman, T. Sejnowski, & G. Hinton (Eds.), Connectionist models: Proceedings of the 1990 summer school (pp. 18–22). San Francisco, CA: Morgan Kaufmann. · DOI
  35. Zemel, R. S. (1993). A minimum description length framework for unsupervised learning. Ph.D. Thesis, Department of Computer Science, University of Toronto, Toronto, Canada.

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Metadata

Authors
Ghahramani, Z., & Jordan, M. I.
Affiliations
Massachusetts Institute of Technology (US)
Venue
Machine Learning
Published
1997-11
Volume
29
Issue
2-3
Pages
245-273
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
OpenAlex
W1528056001
Cited by
1,157
References
3
Citations by year
2026: 4 · 2025: 9 · 2024: 11 · 2023: 12 · 2022: 18 · 2021: 42 · 2020: 44 · 2019: 34 · 2018: 51 · 2017: 67 · 2016: 44 · 2015: 51 · 2014: 72 · 2013: 70 · 2012: 59
FWCI
22.24
Type
journal article
Language
en
Open access
Locations
Machine Learning (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Methods and Mixture Models
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
Keywords
factorial hidden Markov model, posterior inference, forward-backward algorithm, approximate inference, variational approximation, parameter learning, time series modeling, Bach chorales, musical analysis

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