Decision analysis using belief functions
International Journal of Approximate Reasoning
On decision making using belief functions
Advances in the Dempster-Shafer theory of evidence
Risk, Ambiguity, and the Separation of Utility and Beliefs
Mathematics of Operations Research
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Updating beliefs with incomplete observations
Artificial Intelligence
Decision making under uncertainty using imprecise probabilities
International Journal of Approximate Reasoning
An introduction to the imprecise Dirichlet model for multinomial data
International Journal of Approximate Reasoning
Statistical decisions using likelihood information without prior probabilities
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Decision making with partially consonant belief functions
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Probabilistic abduction without priors
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
A new ranking procedure by incomplete pairwise comparisons using preference subsets
Intelligent Data Analysis
Fuzzy logic-based generalized decision theory with imperfect information
Information Sciences: an International Journal
Partially identified prevalence estimation under misclassification using the kappa coefficient
International Journal of Approximate Reasoning
| Hi-index | 0.01 |
The paper presents an efficient solution to decision problems where direct partial information on the distribution of the states of nature is available, either by observations of previous repetitions of the decision problem or by direct expert judgements. To process this information we use a recent generalization of Walley's imprecise Dirichlet model, allowing us also to handle incomplete observations or imprecise judgements, including missing data. We derive efficient algorithms and discuss properties of the optimal solutions with respect to several criteria, including Gamma-maximinity and E-admissibility. In the case of precise data and pure actions the former surprisingly leads us to a frequency-based variant of the Hodges-Lehmann criterion, which was developed in classical decision theory as a compromise between Bayesian and minimax procedures.