WinBUGS – A Bayesian modelling framework: Concepts, structure, and extensibility
Statistics and Computing
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Bayesian learning of Bayesian networks with informative priors
Annals of Mathematics and Artificial Intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic inductive logic programming
Probabilistic inductive logic programming
Approximate bayesian computation for the parameters of PRISM programs
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Variational bayes inference for logic-based probabilistic models on BDDs
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
MCMC estimation of conditional probabilities in probabilistic programming languages
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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We propose a generalMCMC method for Bayesian inference in logic-based probabilistic modeling. It covers a broad class of generativemodels including Bayesian networks and PCFGs. The idea is to generalize an MCMC method for PCFGs to the one for a Turing-complete probabilistic modeling language PRISM in the context of statistical abduction where parse trees are replaced with explanations. We describe how to estimate the marginal probability of data from MCMC samples and how to perform Bayesian Viterbi inference using an example of Naive Bayesmodel augmentedwith a hidden variable.