Lifted Variable Elimination for Probabilistic Logic Programming
نویسندگان
چکیده
منابع مشابه
Lifted Variable Elimination for Probabilistic Logic Programming
Lifted inference has been proposed for various probabilistic logical frameworks in order to compute the probability of queries in a time that depends on the size of the domains of the random variables rather than the number of instances. Even if various authors have underlined its importance for probabilistic logic programming (PLP), lifted inference has been applied up to now only to relationa...
متن کاملMPE and Partial Inversion in Lifted Probabilistic Variable Elimination
It is often convenient to represent probabilistic models in a first-order fashion, using logical atoms such as partners(X,Y ) as random variables parameterized by logical variables. (de Salvo Braz, Amir, & Roth 2005), following (Poole 2003), give a lifted variable elimination algorithm (FOVE) for computing marginal probabilities from first-order probabilistic models (belief assessment, or BA). ...
متن کاملLifted Inference for Probabilistic Programming
A probabilistic program often gives rise to a complicated underlying probabilistic model. Performing inference in such a model is challenging. One solution to this problem is lifted inference which improves tractability by exploiting symmetries in the underlying model. Our group is pursuing a lifted approach to inference for probabilistic logic programs.
متن کاملGeneralized Counting for Lifted Variable Elimination
Lifted probabilistic inference methods exploit symmetries in the structure of probabilistic models to perform inference more efficiently. In lifted variable elimination, the symmetry among a group of interchangeable random variables is captured by counting formulas, and exploited by operations that handle such formulas. In this paper we generalize the structure of counting formulas and present ...
متن کاملCompleteness Results for Lifted Variable Elimination: Appendix
In this document, we present proofs for Theorem 2 and 3 (given in the paper), and provide more explanation for the empirical evaluation. Further, we present a procedure for tramsforming weighted model counting (WMC) models to parfactor models. 1 PROOF OF THEOREM 2 Let us first recall the theorem. Theorem 2 C-FOVE is a complete domain-lifted algorithm for the class of models in which each atom h...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Theory and Practice of Logic Programming
سال: 2014
ISSN: 1471-0684,1475-3081
DOI: 10.1017/s1471068414000283