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arXiv · 1804.06170

On Computing the Total Variation Distance of Hidden Markov Models

Stefan Kiefer
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Abstract

We prove results on the decidability and complexity of computing the total variation distance (equivalently, the $L_1$-distance) of hidden Markov models (equivalently, labelled Markov chains). This distance measures the difference between the distributions on words that two hidden Markov models induce. The main results are: (1) it is undecidable whether the distance is greater than a given threshold; (2) approximation is #P-hard and in PSPACE.

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Stefan Kiefer. 2018-04-17. On Computing the Total Variation Distance of Hidden Markov Models. https://arxiv.org/abs/1804.06170

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