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Links between probabilistic automata and hidden Markov models: probability distributions, learning models and induction algorithms

2005
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Pattern Recognition
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This article presents an overview of Probabilistic Automata (PA) and discrete Hidden Markov Models (HMMs), and aims at clarifying the links between them. The first part of this work concentrates on probability distributions generated by these models. Necessary and sufficient conditions for an automaton to define a probabilistic language are detailed. It is proved that probabilistic deterministic automata (PDFA) form a proper subclass of probabilistic non-deterministic automata (PNFA). Two

doi:10.1016/j.patcog.2004.03.020
fatcat:uytkvkyfpfbj3lhibry6cfwue4