Though hardness results are known for learning distributions generated by general probabilistic automata, we prove that the algorithm we present can efficiently learn distributions generated by PSAs. These processes can be described by a subclass of probabilistic finite automata which we name Probabilistic Suffix Automata (PSA). We propose and analyze a distribution learning algorithm for variable memory length Markov processes.
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