ProPPR: Efficient First-Order Probabilistic Logic Programming for Structure Discovery, Parameter Learning, and Scalable Inference.
William Yang WangKathryn MazaitisWilliam W. CohenPublished in: AAAI Workshop: Statistical Relational Artificial Intelligence (2014)
Keyphrases
- logic programming
- parameter learning
- bayesian networks
- probabilistic reasoning
- factor graphs
- logic programs
- generative model
- probabilistic logic
- structure learning
- classical first order logic
- nonmonotonic reasoning
- conditional random fields
- answer set programming
- logic programming language
- answer sets
- hidden variables
- deductive databases
- statistical learning
- knowledge base
- knowledge representation
- programming language
- maximum likelihood
- first order logic
- em algorithm
- graphical models
- probabilistic graphical models
- stable models
- probabilistic inference
- inductive logic programming
- approximate inference
- posterior probability
- markov random field
- higher order
- markov logic networks
- probabilistic model
- default logic
- bayesian inference
- conditional probabilities
- machine learning
- message passing
- statistical relational learning
- probability distribution
- normal logic programs