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This paper introduces a new method to learn the probabilities defining a BBNs from databases with missing data. The intuition behind this method is close to the robust sensitivity analysis interpretation of probability; the method computes the extreme points of the set of possible distributions consistent with the available information and proceeds by refining this set as more information becomes available. This paper outlines the description of this method and presents some experimental results comparing this approach to the Gibbs Samplings.
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This paper introduces a new method to learn the probabilities defining a BBNs from databases with missing data. The intuition behind this method is close to the robust sensitivity analysis interpretation of probability; the method computes the extreme points of the set of possible distributions cons
## Abstracts and Reviews based on the framework of the BUhlmann and Straubmodel. The results are compared with those obtained by applying the model suggested by Ramlau-Hansen.
## Abstracts and Reviews based on the framework of the BUhlmann and Straubmodel. The results are compared with those obtained by applying the model suggested by Ramlau-Hansen.
## 269 relations over uncertain prospects that (a) are dynamically consistent in the Machina sense and, moreover, for which the updated preferences are also members of this family and (b) can simultaneously accommodate Ellsberg-and Allais-type paradoxes. Replacing the "mixture independence" axiom