Axioms for Probability and Belief-Function Propagation

Shenoy, Prakash P.; Shafer, Glenn

doi:10.1007/978-3-540-44792-4_20

Cited by 185 publications

(206 citation statements)

References 22 publications

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“…Further, in the current analysis it was assumed that there is a single objective (e.g., whether or not an account is fairly stated) and that a single piece of evidence (report from management) is used to evaluate the objective. In situations where there are several interrelated objectives and several interdependent items of evidence, a more appropriate approach would be the use of Bayesian networks 12 (see Pearl, 1986Pearl, , 1988Pearl, , 1990Lauritzen and Spiegelhalter, 1988;Shenoy and Shafer, 1990; and in an auditing context see Dutta and Srivastava, 1993). Finally, future research should examine the use of other approaches such as the Dempster -Shafer Belief Functions framework (Shafer and Srivastava, 1990) in evaluating evidence provided by management.…”

Section: Discussionmentioning

confidence: 99%

A Framework for Assessing the Value of Management Inquiry

Krishnamoorthy

Wright

1999

Int J Auditing

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Using a Bayesian Cascaded Inference approach, this study provides a framework to allow an auditor to simultaneously consider important criteria identified in the literature in evaluating the credibility of evidence obtained from management inquiry. Specifically, the importance of management reliability attributes (competence and objectivity) and the role of misstatement risk are examined. Additionally, fraud risk scenarios are considered.Sensitivity analyses suggest three important propositions that have implications for practice and research. First, reliable management inquiries have the highest potential value when misstatement risks are greatest. Second, in a situation of suspected financial reporting fraud, inquiries from high level management are of little value due to concerns for objectivity. Third, in a suspected defalcation scenario the reliability of higher level management inquiries is contingent upon their competence in identifying suspected fraud, a type of competence that auditors have not traditionally assessed. Copyright

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Section: Discussionmentioning

confidence: 99%

A Framework for Assessing the Value of Management Inquiry

Krishnamoorthy

Wright

1999

Int J Auditing

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“…The belief functions can be joint belief functions as in the original network, as presented in Ref. 22, but they can as well be conditional belief functions. 27 Ž .…”

Section: Belief Network and Evidential Networkmentioning

confidence: 99%

Application of the transferable belief model to diagnostic problems

Smets

1998

Int. J. Intell. Syst.

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“…The Markov tree may look different but the final propagation of beliefs will be the same in all cases. 6 It should be noted that in the case of a tie we have chosen a variable with a one-letter name over a variable with a two-letter name.…”

Section: A Iteration One I =mentioning

confidence: 99%

The belief-function approach to aggregating audit evidence

Srivastava

1995

Int. J. Intell. Syst.

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Abstract:In this article, we present the belief-function approach to aggregating audit evidence. The approach uses an evidential network to represent the structure of audit evidence. In turn, it allows us to treat all types of dependencies and relationships among accounts and items of evidence, and thus the approach should help the auditor conduct an efficient and effective audit. Aggregation of evidence is equivalent to propagation of beliefs in an evidential network. The paper describes in detail the three major steps involved in the propagation process. The first step deals with drawing the evidential network representing the connections among variables and items of evidence, based on the experience and judgment of the auditor. We then use the evidential network to determine the clusters of variables over which we have belief functions. The second step deals with constructing a Markov tree from the clusters of variables determined in step one. The third step deals with the propagation of belief functions in the Markov tree. We use a moderately complex example to illustrate the details of the aggregation process. The Belief-Function Approach to Aggregating Audit Evidence. International Journal of Intelligent Systems, 10 (3), 329-356. Publisher's Official Version: . Open Access Version: . Systems, Vol. 10, No. 3, March 1995, pp. 329-356 The Belief-Function Approach to Aggregating Audit Evidence # The Belief-Function Approach to Aggregating Audit Evidence International Journal of Intelligent ABSTRACTIn this article, we present the belief-function approach to aggregating audit evidence. The approach uses an evidential network to represent the structure of audit evidence. In turn, it allows us to treat all types of dependencies and relationships among accounts and items of evidence, and thus the approach should help the auditor conduct an efficient and effective audit.Aggregation of evidence is equivalent to propagation of beliefs in an evidential network. The paper describes in detail the three major steps involved in the propagation process. The first step deals with drawing the evidential network representing the connections among variables and items of evidence, based on the experience and judgment of the auditor. We then use the evidential network to determine the clusters of variables over which we have belief functions.The second step deals with constructing a Markov tree from the clusters of variables determined in step one. The third step deals with the propagation of belief functions in the Markov tree. We use a moderately complex example to illustrate the details of the aggregation process.

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Axioms for Probability and Belief-Function Propagation

Cited by 185 publications

References 22 publications

A Framework for Assessing the Value of Management Inquiry

A Framework for Assessing the Value of Management Inquiry

Application of the transferable belief model to diagnostic problems

The belief-function approach to aggregating audit evidence

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