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Neural Computation

January 1996, Vol. 8, No. 1, Pages 178-181
(doi: 10.1162/neco.1996.8.1.178)
© 1995 Massachusetts Institute of Technology
Equivalence of Linear Boltzmann Chains and Hidden Markov Models
Article PDF (164.59 KB)
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.