.png){kind=icon}
.png){kind=icon}
Return
Conditional independence structure and its closure: Inferential rules and algorithms
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2009.05.002.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, we deal with conditional independence models closed with respect to graphoid properties. Such models come from different uncertainty measures, in particular in a probabilistic setting. We study some inferential rules and describe methods and algorithms to compute efficiently the closure of a set of conditional independence statements. (C) 2009 Elsevier Inc. All rights reserved.
Keywords:
Conditional independence models
Graphoid properties
Inferential rules
Generalized inclusion
Closure
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3
Papers:
2.9K
Citations:
5.1K