An observed sequence probability estimate in binary linear hidden Markov models with posterior inference in algebraic Bayesian networks

Hidden Markov models (HMM) and algebraic Bayesian networks (ABN) are proba-bilistic graphical models and because of that they are quit similar. HMM haswide application while ABN are not so widespread, but its instruments allow to simulate and solve hidden Markov models problems. The goal of this work is to solve hidden Markov model first problem by means of algebraic Bayesian network posterior inference. An algorithm of estimating probability of observed sequence in binary linear HMM by means of algebraic Bayesian networkposterior inference.