Paper Info
Title
Asymmetric Separation for Local Independence Graphs
Abstract
Abstract Directed possibly cyclic graphs have been proposed by Didelez (2000) and Nodelmann et al. (2002) in order to represent the dynamic dependencies among,stochastic processes. These dependencies are based on a generalization of Granger{causality to continuous time, flrst developed by Schweder (1970) for Markov processes, who called them local dependencies. They deserve special attention as they are asymmetric. In this paper we focus on their graphical representation and develop an asymmetric notion of separation. The properties of this graph separation as well as local independence are investigated in detail within a framework of asymmetric (semi)graphoids allowing insight into what information can be read ofi these graphs.
Year
Venue
Keywords
2006
Uncertainty in Artificial Intelligence
stochastic process,granger causality,markov process
Field
DocType
Volume
Graph,Markov process,Computer science,Stochastic process,Artificial intelligence,Local independence,Machine learning
Conference
abs/1206.6841
Citations 
PageRank 
References 
1
0.36
8
Authors
1
Name
Order
Citations
PageRank
Vanessa Didelez1164.03