We propose statistical abduction as a first-order logical framework for representing, inferring and learning probabilistic knowledge. It semantically integrates logical abduction with a parameterized distribution over abducibles. We show that statistical abduction combined with tabulated search provides an efficient algorithm for probability computation, a Viterbi-like algorithm for finding the most likely explanation, and an EM learning algorithm (the graphical EM algorithm) for learning parameters associated with the distribution which achieve the same computational complexity as those specialized algorithms for HMMs (hidden Markov models), PCFGs (probabilistic context-free grammars) and sc-BNs (singly connected Bayesian networks). © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Sato, T., & Kameya, Y. (2002). Statistical abduction with tabulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2408, pp. 567–587). Springer Verlag. https://doi.org/10.1007/3-540-45632-5_22
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