ML Anthology

Inference and Minimization of Hidden Markov Chains

David Gillman, Michael Sipser
COLT 1994 pp. 147-158
doi:10.1145/180139.181091 /colt/1994/gillman1994colt-inference/

Abstract

A hidden Markov chain (hmc) is a finite ergodic Markov chain in which each of the states is labelled 0 or 1. As the Markov chain moves through a random trajectory, the hmc emits a 0 or a 1 at each times step according to the label of the state just entered.

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Text

Gillman and Sipser. "Inference and Minimization of Hidden Markov Chains." Annual Conference on Computational Learning Theory, 1994. doi:10.1145/180139.181091

Markdown

[Gillman and Sipser. "Inference and Minimization of Hidden Markov Chains." Annual Conference on Computational Learning Theory, 1994.](https://mlanthology.org/colt/1994/gillman1994colt-inference/) doi:10.1145/180139.181091

BibTeX

@inproceedings{gillman1994colt-inference,
  title     = {{Inference and Minimization of Hidden Markov Chains}},
  author    = {Gillman, David and Sipser, Michael},
  booktitle = {Annual Conference on Computational Learning Theory},
  year      = {1994},
  pages     = {147-158},
  doi       = {10.1145/180139.181091},
  url       = {https://mlanthology.org/colt/1994/gillman1994colt-inference/}
}