Name
Abstract | ||
|---|---|---|
The construction of probabilistic models that can represent large systems requires the ability to describe repetitive and hierarchical structures. To do so, one can resort to constructs from description logics. In this paper we present a class of relational Bayesian networks based on the popular description logic DL-Lite. Our main result is that, for this modeling language, marginal inference and most probable explanation require polynomial effort. We show this by reductions to edge covering problems, and derive a result of independent interest; namely, that counting edge covers in a particular class of graphs requires polynomial effort. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23540-0_4 | Lecture Notes in Artificial Intelligence |
Field | DocType | Volume |
Variable elimination,Inference,Computer science,Description logic,Theoretical computer science,Bayesian programming,Bayesian network,Artificial intelligence,Graphical model,Probabilistic logic,Probabilistic relevance model,Machine learning | Conference | 9310 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.37 |
References | Authors | |
20 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Denis Deratani Mauá | 1 | 165 | 24.64 |
| Fábio Cozman | 2 | 18 | 10.16 |