By applying the generalized moments theory we can construct the set of extreme points for a family of probability measures. Posterior functionals which are considered in Bayesian analysis attain their extremes at such points. This is the way to transform optimization with respect to measures into optimization with respect to points in a Euclidean space. The method is applied to known problems of Bayesian robustness as well as to a new class of priors, which is defined by conditions on the marginal distributions of data. © 1994.
Robust Bayesian analysis under generalized moments conditions
Ruggeri Fabrizio;
1994
Abstract
By applying the generalized moments theory we can construct the set of extreme points for a family of probability measures. Posterior functionals which are considered in Bayesian analysis attain their extremes at such points. This is the way to transform optimization with respect to measures into optimization with respect to points in a Euclidean space. The method is applied to known problems of Bayesian robustness as well as to a new class of priors, which is defined by conditions on the marginal distributions of data. © 1994.File in questo prodotto:
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