Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Probabilistic Languages: A Review and Some Open Questions
ACM Computing Surveys (CSUR)
Efficient fixpoint computation in linear tabling
Proceedings of the 5th ACM SIGPLAN international conference on Principles and practice of declaritive programming
Parsing inside-out
Efficient probabilistic top-down and left-corner parsing
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A glimpse of symbolic-statistical modeling by PRISM
Journal of Intelligent Information Systems
PRISM: a language for symbolic-statistical modeling
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Compiling Bayesian networks by symbolic probability calculation based on zero-suppressed BDDs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Inside-outside probability computation for belief propagation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Compiling Bayesian networks with local structure
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Variational bayesian grammar induction for natural language
ICGI'06 Proceedings of the 8th international conference on Grammatical Inference: algorithms and applications
Logic-Based Probabilistic Modeling
WoLLIC '09 Proceedings of the 16th International Workshop on Logic, Language, Information and Computation
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Variational bayes inference for logic-based probabilistic models on BDDs
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
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We propose a logic-based approach to variational Bayes (VB) via propositionalized probability computation in a symbolic-statistical modeling language PRISM. PRISM computes probabilities of logical formulas by reducing them to AND/OR boolean formulas called explanation graphs containing probabilistic ${\tt msw/2}$ atoms. We put Dirichlet priors on parameters of ${\tt msw/2}$ atoms and derive a variational Bayes EM algorithm that learns their hyper parameters from data. It runs on explanation graphs deduced from a program and a goal and computes probabilities in a dynamic programming manner in time linear in the size of the graphs. To show the genericity and effectiveness of Bayesian modeling by the proposed approach, we conducted two learning experiments, one with a probabilistic right-corner grammar and the other with a profile-HMM. To our knowledge, no previous report has been made of VB applied to these models.