Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Statistical Language Learning
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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We present an efficient method for statistical parameter learning of a certain class of symbolic-statistical models (called PRISM programs) including hidden Markov models (HMMs). To learn the parameters, we adopt the EM algorithm, an iterative method for maximum likelihood estimation. For the efficient parameter learning, we first introduce a specialized data structure for explanations for each observation, and then apply a graph-based EM algorithm. The algorithm can be seen as a generalization of Baum-Welch algorithm, an EM algorithm specialized for HMMs. We show that, given appropriate data structure, Baum-Welch algorithm can be simulated by our graph-based EM algorithm.