Artificial Intelligence
OLD resolution with tabulation
Proceedings on Third international conference on logic programming
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamentals of speech recognition
Fundamentals of speech recognition
Probabilistic logic programming
Information and Computation
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Probabilistic deductive databases
ILPS '94 Proceedings of the 1994 International Symposium on Logic programming
Answering queries from context-sensitive probabilistic knowledge bases
Selected papers from the international workshop on Uncertainty in databases and deductive systems
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
From Logic to Logic Programming
From Logic to Logic Programming
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
Effective Bayesian inference for stochastic programs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Loglinear models for first-order probabilistic reasoning
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Statistical Abduction with Tabulation
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
EM Learning for Symbolic-Statistical Models in Statistical Abduction
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Chr(prism)-based probabilistic logic learning
Theory and Practice of Logic Programming
Probabilistic space partitioning in constraint logic programming
ASIAN'04 Proceedings of the 9th Asian Computing Science conference on Advances in Computer Science: dedicated to Jean-Louis Lassez on the Occasion of His 5th Cycle Birthday
Negation elimination for finite PCFGs
LOPSTR'04 Proceedings of the 14th international conference on Logic Based Program Synthesis and Transformation
Logical bayesian networks and their relation to other probabilistic logical models
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
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We have been developing a general symbolic-statistical modeling language [6,19,20] based on the logic programming framework that semantically unifies (and extends) major symbolic-statistical frameworks such as hidden Markov models (HMMs) [18], probabilistic context-free grammars (PCFGs) [23] and Bayesian networks [16]. The language, PRISM, is intended to model complex symbolic phenomena governed by rules and probabilities based on the distributional semantics[19]. Programs contain statistical parameters and they are automatically learned from randomly sampled data by a specially derived EM algorithm, the graphical EM algorithm. It works on support graphs representing the shared structure of explanations for an observed goal. In this paper, we propose the use of tabulation technique to build support graphs, and show that as a result, the graphical EM algorithm attains the same time complexity as specilized EM algorithms for HMMs (the Baum-Welch algorithm [18]) and PCFGs (the Inside-Outside algorithm [1]).