Equivalence of Linear Boltzmann Chains and Hidden Markov Models

It is demonstrated that under the simple condition that the state sequence has a mandatory end state, the probability distribution assign by a strictly linear Boltzmann chain is identical to that assigned by a hidden Markov model.

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.