In the theory of imprecise probability it is often of interest to find the range of the expectation of some function over a convex family of probability measures. Here we show how to find the joint range of the expectations of a finite set of functions when the underlying space is finite and the family of probability distributions is defined by finitely many linear constraints.
|Original language||English (US)|
|State||Published - 2005|
|Event||4th International Symposium on Imprecise Probabilities and Their Applications, ISIPTA 2005 - Pittsburgh, United States|
Duration: Jul 20 2005 → Jul 23 2005
|Conference||4th International Symposium on Imprecise Probabilities and Their Applications, ISIPTA 2005|
|Period||7/20/05 → 7/23/05|
Bibliographical noteFunding Information:
Glen Meeden is a Professor in the School of Statistics at the University of Minnesota, 313 Ford Hall, 224 Church St SE, Minneapolis, MN 55455-0493, USA. E-mail: email@example.com. His research was supported in part by NSF grant DMS-0406169.
© 2005 Society for Imprecise Probability: Theories and Applications, SIPTA. All rights reserved.
Copyright 2019 Elsevier B.V., All rights reserved.
- Convex family of priors
- Linear constraints
- Probability assessment