Connecting dempster–shafer belief functions with likelihood-based inference
Synthese 123 (3):347-364 (2000)
| Abstract | The Dempster–Shafer approach to expressing beliefabout a parameter in a statistical model is notconsistent with the likelihood principle. Thisinconsistency has been recognized for some time, andmanifests itself as a non-commutativity, in which theorder of operations (combining belief, combininglikelihood) makes a difference. It is proposed herethat requiring the expression of belief to be committed to the model (and to certain of itssubmodels) makes likelihood inference very nearly aspecial case of the Dempster–Shafer theory. | |||||||||
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R. Yager, M. Fedrizzi & J. Kacprzyk (eds.) (1994). Advances in the Dempster- Shafer Theory of Evidence. John Wiley.
Rolf Haenni & Stephan Hartmann (2006). Modeling Partially Reliable Information Sources: A General Approach Based on Dempster-Shafer Theory. Information Fusion 7:361-379.
Arthur Dempster (2008). The Dempster-Shafer Calculus for Statisticians. International Journal of Approximate Reasoning 48 (2):365--377.
Jonas Clausen Mork (2013). Uncertainty, Credal Sets and Second Order Probability. Synthese 190 (3):353-378.
Glenn Shafer (1981). Jeffrey's Rule of Conditioning. Philosophy of Science 48 (3):337-362.
J. Y. Jaffray (1994). Decision Making with Belief Functions. In R. Yager, M. Fedrizzi & J. Kacprzyk (eds.), Advances in the Dempster- Shafer Theory of Evidence. John Wiley.
Glenn Shafer (2010). A Betting Interpretation for Probabilities and Dempster-Shafer Degrees of Belief. International Journal of Approximate Reasoning.
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