Bounds for Expected Loss in Bayesian Decision Theory with Imprecise Prior Probabilities
Classical Bayesian inference uses the expected value of a loss function with regard to a single prior distribution for a parameter to compare decisions, and an optimal decision minimizes the expected loss. Recently interest has grown in generalizations of this framework without specified priors, to...
| Veröffentlicht in: | Journal of the Royal Statistical Society. Series D (The Statistician). - Carfax Publishing Co., 1962. - 43(1994), 3, Seite 371-379 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
1994
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| Zugriff auf das übergeordnete Werk: | Journal of the Royal Statistical Society. Series D (The Statistician) |
| Schlagworte: | Bayesian Decision Theory Imprecise Probabilities Intervals of Measures Lower and Upper Bounds for Expected Loss Mathematics Philosophy Behavioral sciences Information science Physical sciences |