Newsgroups: comp.ai.jair.announce
Path: cantaloupe.srv.cs.cmu.edu!rochester!cornellcs!newsstand.cit.cornell.edu!news.acsu.buffalo.edu!dsinc!spool.mu.edu!newspump.sol.net!howland.erols.net!feed1.news.erols.com!news.dra.com!news.he.net!cnn.nas.nasa.gov!eos!kronos.arc.nasa.gov!jair-ed
From: jair-ed@ptolemy.arc.nasa.gov
Subject: New Article, Exploiting Causal Independence in ...
Message-ID: <1996Dec11.185756.12455@ptolemy-ethernet.arc.nasa.gov>
Originator: jair-ed@polya.arc.nasa.gov
Lines: 40
Sender: usenet@ptolemy-ethernet.arc.nasa.gov (usenet@ptolemy.arc.nasa.gov)
Nntp-Posting-Host: polya.arc.nasa.gov
Organization: NASA/ARC Computational Sciences Division
Date: Wed, 11 Dec 1996 18:57:56 GMT
Approved: jair-ed@ptolemy.arc.nasa.gov

JAIR is pleased to announce the publication of the following article:

Zhang, N.L. and Poole, D. (1996)
  "Exploiting Causal Independence in Bayesian Network Inference", 
   Volume 5, pages 301-328.

   Available in HTML, Postscript (276K) and compressed Postscript (113K).
   For quick access via your WWW browser, use this URL:
     http://www.cs.washington.edu/research/jair/abstracts/zhang96a.html
   More detailed instructions are below.

   Abstract: A new method is proposed for exploiting causal
   independencies in exact Bayesian network inference.  A Bayesian
   network can be viewed as representing a factorization of a joint
   probability into the multiplication of a set of conditional
   probabilities.  We present a notion of causal independence that
   enables one to further factorize the conditional probabilities into a
   combination of even smaller factors and consequently obtain a
   finer-grain factorization of the joint probability.  The new
   formulation of causal independence lets us specify the conditional
   probability of a variable given its parents in terms of an associative
   and commutative operator, such as ``or'', ``sum'' or ``max'', on the
   contribution of each parent.  We start with a simple algorithm VE for
   Bayesian network inference that, given evidence and a query variable,
   uses the factorization to find the posterior distribution of the
   query. We show how this algorithm can be extended to exploit causal
   independence. Empirical studies, based on the CPCS networks for
   medical diagnosis, show that this method is more efficient than
   previous methods and allows for inference in larger networks than
   previous algorithms.

The article is available via:
   
 -- comp.ai.jair.papers (also see comp.ai.jair.announce)

 -- World Wide Web: The URL for our World Wide Web server is
       http://www.cs.washington.edu/research/jair/home.html
    For direct access to this article and related files try:
       http://www.cs.washington.edu/research/jair/abstracts/zhang96a.html

 -- Anonymous FTP from either of the two sites below.

    Carnegie-Mellon University (USA):
	ftp://ftp.cs.cmu.edu/project/jair/volume5/zhang96a.ps
    The University of Genoa (Italy):
	ftp://ftp.mrg.dist.unige.it/pub/jair/pub/volume5/zhang96a.ps

    The compressed PostScript file is named zhang96a.ps.Z (113K)

 -- automated email. Send mail to jair@cs.cmu.edu or jair@ftp.mrg.dist.unige.it
    with the subject AUTORESPOND and our automailer will respond. To
    get the Postscript file, use the message body GET volume5/zhang96a.ps 
    (Note: Your mailer might find this file too large to handle.) 
    Only one can file be requested in each message.

For more information about JAIR, visit our WWW or FTP sites, or
send electronic mail to jair@cs.cmu.edu with the subject AUTORESPOND
and the message body HELP, or contact jair-ed@ptolemy.arc.nasa.gov.



