Neural Computation
January 1996, Vol. 8, No. 1, Pages 178-181
(doi: 10.1162/neco.1996.8.1.178)
Equivalence of Linear Boltzmann Chains and Hidden Markov Models
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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.