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COMPUTATION OF BEST BOUNDS OF PROBABILITIES FROM UNCERTAIN DATA
Author(s) -
Luo Chengjie,
Lobo Clement Yu, and Jorge,
Wang Gaoming,
Pham Tracy,
Yu Clement
Publication year - 1996
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.1996.tb00276.x
Subject(s) - upper and lower bounds , computation , conditional probability , reduction (mathematics) , range (aeronautics) , mathematics , set (abstract data type) , computer science , algorithm , mathematical optimization , statistics , mathematical analysis , materials science , geometry , composite material , programming language
An uncertainty reasoning method is presented in this article. The method can be used to compute from a given set of conditional probabilities the best lower bounds and the best upper bounds of those conditional probabilities that are not explicitly provided. The computation of the best upper(lower) bound of such a conditional probability relies on solution of a linear programming problem. Some reduction techniques are proposed in this article to improve the efficiency of our uncertainty reasoning method. As illustrated in Section 4.3, for many uncertainty reasoning problems in medical diagnosis, by using our reduction techniques, the best range of a conditional probability, which is specified by a lower bound and an upper bound, can be computed in polynomial time in terms of the number of basic events involved in the reasoning.