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Author Santos, Eugene
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©1996
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Artificial intelligence ♦ Data compaction and compression ♦ Integer programming ♦ Least squares approximation ♦ Pattern recognition ♦ Probabilistic reasoning ♦ Uncertainty
Abstract Probabilistic reasoning suffers from NP-hard implementations. In particular, the amount of probabilistic information necessary to the computations is often overwhelming. For example, the size of conditional probability tables in Bayesian networks has long been a limiting factor in the general use of these networks.We present a new approach for manipulating the probabilistic information given. This approach avoids being overwhelmed by essentially compressing the information using approximation functions called linear potential functions. We can potentially reduce the information from a combinatorial amount to roughly linear in the number of random variable assigments. Furthermore, we can compute these functions through closed form equations. As it turns out, our approximation method is quite general and may be applied to other data compression problems.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1996-05-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 43
Issue Number 3
Page Count 32
Starting Page 399
Ending Page 430


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Source: ACM Digital Library