Sensitivity analysis for threshold decision making with Bayesian belief networks

Linda C. Van Der Gaag, Veerle M.H. Coupé

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

19 Citations (Scopus)


The probability assessments of a Bayesian belief network generally include inaccuracies. These inaccuracies influence the reliability of the network's output. An integral part of investigating the output's reliability is to study its robustness. Robustness pertains to the extent to which varying the probability assessments of the network influences the output. It is studied by subjecting the network to a sensitivity analysis. In this paper, we address the issue of robustness of a belief network's output in view of the threshold model for decision making. We present a method for sensitivity analysis that provides for the computation of bounds between which a network's assessments can be varied without inducing a change in recommended decision.

Original languageEnglish
Title of host publicationAI*IA 99
Subtitle of host publicationAdvances in Artificial Intelligence - 6th Congress of Italian Association for Artificial Intelligence, Selected Papers
Number of pages12
Publication statusPublished - 2000
Event6th Congress of the Italian Association for Artificial Intelligence, AI*IA 99 - Bologna, Italy
Duration: 14 Sept 199917 Sept 1999

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1792 LNAI


Conference6th Congress of the Italian Association for Artificial Intelligence, AI*IA 99

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