A Comparison of Methods for Transforming Belief Function Models to Probability Models
View/ Open
Issue Date
2003-07Author
Cobb, Barry R.
Shenoy, Prakash P.
Publisher
Springer-Verlag
Format
1245764 bytes
Type
Book chapter
Is part of series
Lecture Notes in Artificial Intelligence;No. 2711
Metadata
Show full item recordAbstract
Recently, we proposed a new method called the plausibility transformation method to convert a belief function model to an equivalent probability model. In this paper, we compare the plausibility
transformation method with the pignistic transformation method. The two transformation methods yield qualitatively di®erent probability
models. We argue that the plausibility transformation method is the correct method for translating a belief function model to an equivalent probability model that maintains belief function semantics.
Description
This 12-pp paper is extracted from a longer unpublished working paper: "On Transforming Belief Function Models to Probability Models," School of Business Working Paper No. 293, July 2003, University of Kansas, Lawrence, KS.
ISBN
3-540-40494-5ISSN
0302-9743Collections
Citation
Cobb, B. R. and P. P. Shenoy (2003), "A Comparison of Methods for Transforming Belief Function Models to Probability Models," in T. D. Nielsen and N. L. Zhang (eds.), Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Lecture Notes in Artificial Intelligence No. 2711, pp. 255--266, Springer-Verlag, Berlin.
Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.