Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Conditional independence and chain event graphs

Tools
- Tools
+ Tools

Smith, J. Q. and Anderson, Paul E. (2008) Conditional independence and chain event graphs. Artificial Intelligence, Vol.172 (No.1). pp. 42-68. doi:10.1016/j.artint.2007.05.004

Research output not available from this repository, contact author.
Official URL: http://dx.doi.org/10.1016/j.artint.2007.05.004

Request Changes to record.

Abstract

Graphs provide an excellent framework for interrogating symmetric models of measurement random. variables and discovering their implied conditional independence structure. However, it is not unusual for a model to be specified from a description of how a process unfolds (i.e. via its event tree), rather than through relationships between a given set of measurements. Here we introduce a new mixed graphical structure called the chain event graph that is a function of this event tree and a set of elicited equivalence relationships. This graph is more expressive and flexible than either the Bayesian network-equivalent in the symmetric case-or the probability decision graph. Various separation theorems are proved for the chain event graph. These enable implied conditional independencies to be read from the graph's topology. We also show how the topology can be exploited to tease out the interesting conditional independence structure of functions of random variables associated with the underlying event tree. (c) 2007 Elsevier B.V. All rights reserved.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Graph theory, Random variables, Probabilites, Bayesian statistical decision theory
Journal or Publication Title: Artificial Intelligence
Publisher: Elsevier BV
ISSN: 0004-3702
Official Date: January 2008
Dates:
DateEvent
January 2008Published
Volume: Vol.172
Number: No.1
Number of Pages: 27
Page Range: pp. 42-68
DOI: 10.1016/j.artint.2007.05.004
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access

Data sourced from Thomson Reuters' Web of Knowledge

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item
twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us