Readers of this list may be interested in an article recently published by
JAIR:
Chan, H. and Darwiche, A. (2002)
"When do Numbers Really Matter?", Volume 17, pages 265-287.
Available in PDF, PostScript and compressed PostScript.
For quick access via your WWW browser, use this URL:
http://www.jair.org/abstracts/chan02a.html
More detailed instructions are below.
Abstract: Common wisdom has it that small distinctions in the
probabilities (parameters) quantifying a belief network do not matter
much for the results of probabilistic queries. Yet, one can develop
realistic scenarios under which small variations in network parameters
can lead to significant changes in computed queries. A pending
theoretical question is then to analytically characterize parameter
changes that do or do not matter. In this paper, we study the
sensitivity of probabilistic queries to changes in network parameters
and prove some tight bounds on the impact that such parameters can
have on queries. Our analytic results pinpoint some interesting
situations under which parameter changes do or do not matter. These
results are important for knowledge engineers as they help them
identify influential network parameters. They also help explain some
of the previous experimental results and observations with regards to
network robustness against parameter changes.
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.jair.org/
For direct access to this article and related files try:
http://www.jair.org/abstracts/chan02a.html
-- Anonymous FTP from Carnegie-Mellon University (USA):
ftp://ftp.cs.cmu.edu/project/jair/volume17/chan02a.ps
The compressed PostScript file is named chan02a.ps.Z (519K)
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