Sensitivity analysis in discrete Bayesian networks
This paper presents an efficient computational method for performing sensitivity analysis in discrete Bayesian networks. The method exploits the structure of conditional probabilities of a target node given the evidence. First, the set of parameters which is relevant to the calculation of the condit...
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| Published in | IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 27; no. 4; pp. 412 - 423 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
IEEE
01.07.1997
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper presents an efficient computational method for performing sensitivity analysis in discrete Bayesian networks. The method exploits the structure of conditional probabilities of a target node given the evidence. First, the set of parameters which is relevant to the calculation of the conditional probabilities of the target node is identified. Next, this set is reduced by removing those combinations of the parameters which either contradict the available evidence or are incompatible. Finally, using the canonical components associated with the resulting subset of parameters, the desired conditional probabilities are obtained. In this way, an important saving in the calculations is achieved. The proposed method can also be used to compute exact upper and lower bounds for the conditional probabilities, hence a sensitivity analysis can be easily performed. Examples are used to illustrate the proposed methodology. |
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| AbstractList | This paper presents an efficient computational method for performing sensitivity analysis in discrete Bayesian networks. The method exploits the structure of conditional probabilities of a target node given the evidence. First, the set of parameters which is relevant to the calculation of the conditional probabilities of the target node is identified. Next, this set is reduced by removing those combinations of the parameters which either contradict the available evidence or are incompatible. Finally, using the canonical components associated with the resulting subset of parameters, the desired conditional probabilities are obtained. In this way, an important saving in the calculations is achieved. The proposed method can also be used to compute exact upper and lower bounds for the conditional probabilities, hence a sensitivity analysis can be easily performed. Examples are used to illustrate the proposed methodology. This paper presents an efficient computational method for performing sensitivity analysis in discrete Bayesian networks. The method exploits the structure of conditional probabilities of a target node given the evidence. First, the set of parameters which is relevant to the calculation of the conditional probabilities of the target node is identified. Next, this set is reduced by removing those combinations of the parameters which either contradict the available evidence or are incompatible. Finally, using the canonical components associated with the resulting subset of parameters, the desired conditional probabilities are obtained. In this way, an important saving in the calculations is achieved. The proposed method can also be used to compute exact upper and lower bounds for the conditional probabilities, hence a sensitivity analysis can be easily performed. Examples are used to illustrate the proposed methodology |
| Author | Castillo, E. Gutierrez, J.M. Hadi, A.S. |
| Author_xml | – sequence: 1 givenname: E. surname: Castillo fullname: Castillo, E. organization: Dept. of Appl. Math. & Comput. Sci., Cantabria Univ., Santander, Spain – sequence: 2 givenname: J.M. surname: Gutierrez fullname: Gutierrez, J.M. – sequence: 3 givenname: A.S. surname: Hadi fullname: Hadi, A.S. |
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| Cites_doi | 10.1109/21.384253 10.1002/net.3230200504 10.1002/net.3230200505 10.1016/B978-1-55860-332-5.50070-5 10.1002/nav.3800110204 10.1016/0888-613X(94)90019-1 10.1109/21.384252 10.1002/net.3230200503 10.1007/3-540-60112-0_11 10.1002/net.3230200507 10.1016/0888-613X(92)90006-L 10.1016/0004-3702(86)90072-X |
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| References | ref13 ref15 ref14 ref11 ref10 lauritzen (ref9) 1988; 50 ref1 ref16 ref8 ref7 ref3 ref6 ref5 pearl (ref12) 1988 fertig (ref4) 1990 castillo (ref2) 1996 |
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| Title | Sensitivity analysis in discrete Bayesian networks |
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