Conference item icon

Conference item

Compositional probabilistic and causal inference using tractable circuit models

Abstract:

Probabilistic circuits (PCs) are a class of tractable probabilistic models, which admit efficient inference routines depending on their structural properties. In this paper, we introduce md-vtrees, a novel structural formulation of (marginal) determinism in structured decomposable PCs, which generalizes previously proposed classes such as probabilistic sentential decision diagrams. Crucially, we show how md-vtrees can be used to derive tractability conditions and efficient algorithms for adva...

Expand abstract
Publication status:
Published
Peer review status:
Peer reviewed

Actions


Access Document


Authors


More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
Trinity College
Role:
Author
ORCID:
0000-0001-9022-7599
More from this funder
Name:
European Commission
Grant:
834115
Publisher:
Journal of Machine Learning Research
Host title:
Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023)
Series number:
Proceedings of Machine Learning Research
Volume:
206
Issue:
2023
Pages:
9488-9498
Publication date:
2023-05-10
Acceptance date:
2023-01-20
Event title:
26th International Conference on Artificial Intelligence and Statistics (AISTATS)
Event location:
Valencia, Spain
Event website:
http://aistats.org/aistats2023/
Event start date:
2023-04-25
Event end date:
2023-04-27
ISSN:
2640-3498
Language:
English
Keywords:
Pubs id:
1341401
Local pid:
pubs:1341401
Deposit date:
2023-05-17

Terms of use


Views and Downloads






If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP