Lifted Probabilistic Inference with Counting Formulas
Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries efficiently. Previous work such as de Salvo Braz et al.'s first-order variable elimination (FOVE) has focused on the sharing of potentials across interchangeable random variables. In this paper, we...
Main Authors: | Haimes, Michael M., Kaelbling, Leslie P., Kersting, Kristian, Milch, Brian, Zettlemoyer, Luke S. |
---|---|
Other Authors: | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
Format: | Article |
Language: | en_US |
Published: |
AAAI Press
2012
|
Online Access: | http://hdl.handle.net/1721.1/72028 https://orcid.org/0000-0001-6054-7145 |
Similar Items
-
Lifted Probabilistic Inference with Counting Formulas
by: Milch, Brian, et al.
Published: (2014) -
Learning probabilistic relational planning rules
by: Zettlemoyer, Luke S. (Luke Sean), 1978-
Published: (2014) -
The string landscape: On formulas for counting vacua
by: Friedmann, Tamar, et al.
Published: (2017) -
Counting solutions to random CNF formulas
by: Galanis, A, et al.
Published: (2021) -
Softstar: Heuristic-guided probabilistic inference
by: Monfort, Mathew, et al.
Published: (2017)