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ISIPTA
2005
IEEE

Decision making under incomplete data using the imprecise Dirichlet model

9 years 5 months ago
Decision making under incomplete data using the imprecise Dirichlet model
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. We derive efficient algorithms and discuss properties of the optimal solutions. In the case of precise data and pure actions we are surprisingly led 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. Keywords. Belief functions, coarse data, decision making, Hodges-Lehmann criterion, imperfect measurements, imprecise Dirichlet model, imprecise probabilities, incomplete data, interval probability, interval statistical models, predictive probabiliti...
Lev V. Utkin, Thomas Augustin
Added 25 Jun 2010
Updated 25 Jun 2010
Type Conference
Year 2005
Where ISIPTA
Authors Lev V. Utkin, Thomas Augustin
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