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
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Anti-unification in constraint logics: foundations and applications to learnability in first-order logic, to speed-up learning, and to deduction
Bucket elimination: a unifying framework for reasoning
Artificial Intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Probabilistic Logic Programming and Bayesian Networks
ACSC '95 Proceedings of the 1995 Asian Computing Science Conference on Algorithms, Concurrency and Knowledge
Bayesian Logic Programs
On applying or-parallelism and tabling to logic programs
Theory and Practice of Logic Programming
Machine Learning
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
nFOIL: integrating Naïve Bayes and FOIL
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning probabilities for noisy first-order rules
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Journal of Artificial Intelligence Research
Parameter learning of logic programs for symbolic-statistical modeling
Journal of Artificial Intelligence Research
Variational probabilistic inference and the QMR-DT network
Journal of Artificial Intelligence Research
Compiling Bayesian networks using variable elimination
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Change of representation for statistical relational learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
View learning for statistical relational learning: with an application to mammography
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Generative modeling with failure in PRISM
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Learning structure and parameters of stochastic logic programs
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Probabilistic inductive logic programming
New advances in logic-based probabilistic modeling by PRISM
Probabilistic inductive logic programming
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
Inference with constrained hidden markov models in prism
Theory and Practice of Logic Programming
Theory and Practice of Logic Programming - Prolog Systems
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In Datalog, missing values are represented by Skolem constants. More generally, in logic programming missing values, or existentially quantified variables, are represented by terms built from Skolem functors. The CLP(BN) language represents the joint probability distribution over missing values in a database or logic program by using constraints to represent Skolem functions. Algorithms from inductive logic programming (ILP) can be used with only minor modification to learn CLP(BN) programs. An implementation of CLP(BN) is publicly available as part of YAP Prolog at http://www.ncc.up.pt/~vsc/Yap.