Equivalence of Linear Boltzmann Chains and Hidden Markov Models

MacKay, David J. C.

doi:10.1162/NECO.1996.8.1.178

Equivalence of Linear Boltzmann Chains and Hidden Markov Models

David J. C. MacKay

NeCo 1996 pp. 178-181

doi:10.1162/NECO.1996.8.1.178 /neco/1996/mackay1996neco-equivalence/

Abstract

Several authors have studied the relationship between hidden Markov models and “Boltzmann chains” with a linear or “time-sliced” architecture. Boltzmann chains model sequences of states by defining state-state transition energies instead of probabilities. In this note I demonstrate that under the simple condition that the state sequence has a mandatory end state, the probability distribution assigned by a strictly linear Boltzmann chain is identical to that assigned by a hidden Markov model.

NeCo Semantic Scholar

Cite

Text

MacKay. "Equivalence of Linear Boltzmann Chains and Hidden Markov Models." Neural Computation, 1996. doi:10.1162/NECO.1996.8.1.178

Markdown

[MacKay. "Equivalence of Linear Boltzmann Chains and Hidden Markov Models." Neural Computation, 1996.](https://mlanthology.org/neco/1996/mackay1996neco-equivalence/) doi:10.1162/NECO.1996.8.1.178

BibTeX

@article{mackay1996neco-equivalence,
  title     = {{Equivalence of Linear Boltzmann Chains and Hidden Markov Models}},
  author    = {MacKay, David J. C.},
  journal   = {Neural Computation},
  year      = {1996},
  pages     = {178-181},
  doi       = {10.1162/NECO.1996.8.1.178},
  volume    = {8},
  url       = {https://mlanthology.org/neco/1996/mackay1996neco-equivalence/}
}