In a number of fields one is to compare a witness string with a distribution. One possibility is to compute the probability of the string for that distribution. Another, giving a more global view, is to compute the expected edit distance from a string randomly drawn to the witness string. This number is often used to measure the performance of a prediction, the goal then being to return the median string, or the string with smallest expected distance. To be able to measure this, computing the distance between a hypothesis and that distribution is necessary. This paper proposes two solutions for computing this value, when the distribution is defined with a probabilistic finite state automaton. The first is exact but has a cost which can be exponential in the length of the input string, whereas the second is a Fpras.
CITATION STYLE
Calvo-Zaragoza, J., De La Higuera, C., & Oncina, J. (2016). Computing the expected edit distance from a string to a PFA. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9705, pp. 39–50). Springer Verlag. https://doi.org/10.1007/978-3-319-40946-7_4
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