Variational bayes inference for logic-based probabilistic models on BDDs

Authors:
Masakazu Ishihata;Yoshitaka Kameya;Taisuke Sato
Affiliations:
Tokyo institute of Technology, Meguro-ku, Tokyo, Japan;Tokyo institute of Technology, Meguro-ku, Tokyo, Japan;Tokyo institute of Technology, Meguro-ku, Tokyo, Japan
Venue:
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Year:
2011

Citing 13
Cited 0

Probabilistic Horn abduction and Bayesian networks

Artificial Intelligence
The independent choice logic for modelling multiple agents under uncertainty

Artificial Intelligence - Special issue on economic principles of multi-agent systems
Towards Combining Inductive Logic Programming with Bayesian Networks

ILP '01 Proceedings of the 11th International Conference on Inductive Logic Programming
Markov logic networks

Machine Learning
Application of abductive ILP to learning metabolic network inhibition from temporal data

Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)

Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Binary Decision Diagrams

IEEE Transactions on Computers
Variational Bayes via propositionalized probability computation in PRISM

Annals of Mathematics and Artificial Intelligence
Parameter learning of logic programs for symbolic-statistical modeling

Journal of Artificial Intelligence Research
ProbLog: a probabilistic prolog and its application in link discovery

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Evaluating abductive hypotheses using an EM algorithm on BDDs

IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Abductive plan recognition by extending Bayesian logic programs

ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
A general MCMC method for Bayesian inference in logic-based probabilistic modeling

IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two

Quantified Score

Hi-index	0.00

Visualization

Abstract

Abduction is one of the basic logical inferences (deduction, induction and abduction) and derives the best explanations for our observation. Statistical abduction attempts to define a probability distribution over explanations and to evaluate them by their probabilities. Logic-based probabilistic models (LBPMs) have been developed as a way to combine probabilities and logic, and it enables us to perform statistical abduction. However non-deterministic knowledge like preference and frequency seems difficult to represent by logic. Bayesian inference can reflect such knowledge on a prior distribution, and variational Bayes (VB) is known as an approximation method for it. In this paper, we propose VB for logic-based probabilistic models and show that our proposed method is efficient in evaluating abductive explanations about failure in a logic circuit and a metabolic pathway.