Boltzmann Chains and Hidden Markov Models

  title={Boltzmann Chains and Hidden Markov Models},
  author={Lawrence K. Saul and Michael I. Jordan},
We propose a statistical mechanical framework for the modeling of discrete time series. Maximum likelihood estimation is done via Boltzmann learning in one-dimensional networks with tied weights. We call these networks Boltzmann chains and show that they contain hidden Markov models (HMMs) as a special case. Our framework also motivates new architectures that address particular shortcomings of HMMs. We look at two such architectures: parallel chains that model feature sets with disparate time… CONTINUE READING


Publications referenced by this paper.
Showing 1-10 of 11 references

Equivalence of Boltzmann Chains and Hidden Markov Models, submitted to Neural Compo

  • D. J. MacKay
  • 1994
2 Excerpts

Links Between Dynamic Programming and Statistical Physics for Heterogeneous Systems, JPL/Caltech

  • P. Stolorz
  • 1994
1 Excerpt

Mean Field Networks That Learn To Discriminate Temporally Distorted Strings

  • C. Williams, G. E. Hinton
  • Proc. Connectionist Models Summer
  • 1990
1 Excerpt

An Inequality and Associated Maximization Technique in Statistical Estimation of Probabilistic Functions of Markov Processes

  • W. Byrne
  • Inequalities
  • 1972

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