Oct. 18 2005 2:00PM

Oct. 18 2005 3:30PM

souhrn: In applications of Bayesian analysis one problem that arises is the evaluation of the sensitivity, or robustness, of the adopted inferential procedure with respect to the components of the formulated statistical model. In particular, it is of interest to study robustness with respect to the prior, when this latter cannot be uniquely elicited, but a whole class of probability measures, agreeing with the available information, can be identified. In this situation, the analysis of robustness consists of finding the extreme of posterior functionals under the class of prior distributions.
In the lecture we provide a theoretical framework for the treatment of a global robustness problem in the context of hierarchical mixture modeling, where the mixing distribution is a random probability whose law belongs to a generalized moment class. Under suitable conditions, the solution of this latter coincides with the solution of a linear semi-infinite programming problem.

Pravidelný seminář Rozhodování a řízení za neurčitosti. (Contact person: Miroslav Kárný)