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Introduction to assessing uncertainty
Journal of §tatistical Plaming and Inference 20 (1988)
George
241
University, Fairfm, VA 22030, U.S.A.
erbert S Stanford University, Stanford, CA 94305, W.S.A.
The nature and assessment of uncertai which relatively few investigators care to ---__z_ exceptionalkj difficult to give an uneyulJoca1, @leai-Cti"r philosophically rich topic 1 ing most inves comparatively straightforwa world of mathematics a theless, the assessment of uncertainty has beco of the emergence of artificial intellingence and the need to graft some computationally reasonable, intellectually satisfying treatment uncertainty onto the rule-based expert system paradigm. In large part, then, t stimulation of interest in these foundational issues traces its motivation back to t tion. ith this application in mind, a California. The rew up the list of
242
Intmduction
erspective on the expert system application. The remaining papers all represent vember 13 and 1 papers given on man represents a critical look at the The paper by The point is made that rando ness and determinism can nt is illustrated by a discussion of ch settin cusses the relations of randomness to co ness. introduces a notion o
ecislon theory. The preferences) from groups of individuals when a joint decision is paper provides a summary of a number of recent opments, both ublished and unpublished, on m~~!tibayesianity. The paper of fessor Zabell is focused on probabilistic analysis C.4 ~estiuisny-. LWxli reviews some of the historical treatments of the probabilistic analysis of testimony and again focuses on the probability assigned to combined evidence from several witnesses. Throughout these three papers there is a common theme of the propability of combinations of uncertain evidence or opinion with its clear application to assessment of probabilities in expert systems. -Shafer belief functions and The next three papers deal with the ng treat directly the theory of empster and Aug variants of it. belief functio s applied to an artificial analyst (i.e. an expert system) and represented by a network in a special form (a tree of cliques). This representation locally controlled hm capable of simultaneous (i.e. parallel) co owrance, in turn, discusses a formal basis implementation. ated reasonning systems based upon belief function. The ussed, known as
3
George
241
University, Fairfm, VA 22030, U.S.A.
erbert S Stanford University, Stanford, CA 94305, W.S.A.
The nature and assessment of uncertai which relatively few investigators care to ---__z_ exceptionalkj difficult to give an uneyulJoca1, @leai-Cti"r philosophically rich topic 1 ing most inves comparatively straightforwa world of mathematics a theless, the assessment of uncertainty has beco of the emergence of artificial intellingence and the need to graft some computationally reasonable, intellectually satisfying treatment uncertainty onto the rule-based expert system paradigm. In large part, then, t stimulation of interest in these foundational issues traces its motivation back to t tion. ith this application in mind, a California. The rew up the list of
242
Intmduction
erspective on the expert system application. The remaining papers all represent vember 13 and 1 papers given on man represents a critical look at the The paper by The point is made that rando ness and determinism can nt is illustrated by a discussion of ch settin cusses the relations of randomness to co ness. introduces a notion o
ecislon theory. The preferences) from groups of individuals when a joint decision is paper provides a summary of a number of recent opments, both ublished and unpublished, on m~~!tibayesianity. The paper of fessor Zabell is focused on probabilistic analysis C.4 ~estiuisny-. LWxli reviews some of the historical treatments of the probabilistic analysis of testimony and again focuses on the probability assigned to combined evidence from several witnesses. Throughout these three papers there is a common theme of the propability of combinations of uncertain evidence or opinion with its clear application to assessment of probabilities in expert systems. -Shafer belief functions and The next three papers deal with the ng treat directly the theory of empster and Aug variants of it. belief functio s applied to an artificial analyst (i.e. an expert system) and represented by a network in a special form (a tree of cliques). This representation locally controlled hm capable of simultaneous (i.e. parallel) co owrance, in turn, discusses a formal basis implementation. ated reasonning systems based upon belief function. The ussed, known as
3