Name
Abstract | ||
|---|---|---|
Bayesian networks constitute a qualitative representation for conditional independence CI properties of a probability distribution. It is known that every CI statement implied by the topology of a Bayesian network G is witnessed over G under a graph-theoretic criterion called d-separation. Alternatively, all such implied CI statements have been shown to be derivable using the so-called semi-graphoid axioms. In this article we consider Labeled Directed Acyclic Graphs LDAG the purpose of which is to graphically model situations exhibiting context-specific independence CSI. We define an analogue of dependence logic suitable to express context-specific independence and study its basic properties. We also consider the problem of finding inference rules for deriving non-local CSI and CI statements that logically follow from the structure of a LDAG but are not explicitly encoded by it. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-662-52921-8_11 | WoLLIC |
Field | DocType | Volume |
Discrete mathematics,Logical approach,Conditional independence,Computer science,Axiom,Algorithm,Directed acyclic graph,Probability distribution,Dependence logic,Bayesian network,Rule of inference | Conference | 9803 |
ISSN | Citations | PageRank |
0302-9743 | 3 | 0.39 |
References | Authors | |
18 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| jukka corander | 1 | 302 | 32.66 |
| Antti Hyttinen | 2 | 97 | 12.55 |
| Juha Kontinen | 3 | 176 | 24.67 |
| Pensar, Johan | 4 | 19 | 4.76 |
| Jouko Väänänen | 5 | 131 | 20.60 |