Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
Stochastic dominance and expected utility: survey and analysis
Management Science
A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic Beliefs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A Comparison of Axiomatic Approaches to Qualitative Decision Making Using Possibility Theory
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Decision making on the sole basis of statistical likelihood
Artificial Intelligence
Decision making under incomplete data using the imprecise Dirichlet model
International Journal of Approximate Reasoning
Decision making on the sole basis of statistical likelihood
Artificial Intelligence
A decision theory for partially consonant belief functions
International Journal of Approximate Reasoning
Decision making with partially consonant belief functions
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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This paper presents a decision-theoretic approach to statistical inference that satisfies the Likelihood Principle (LP) without using prior information. Unlike the Bayesian approach, which also satisfies LP, we do not assume knowledge of the prior distribution of the unknown parameter. With respect to information that can be obtained from an experiment, our solution is more efficient than Wald's minimax solution. However, with respect to information assumed to be known before the experiment, our solution demands less input than the Bayesian solution.