FOURTH INTERNATIONAL SYMPOSIUM ON
IMPRECISE PROBABILITIES AND THEIR APPLICATIONS
Carnegie Mellon University
Pittsburgh, PA, USA
July 20-23 2005

ISIPTA'05 ELECTRONIC PROCEEDINGS

Erik Quaeghebeur, Gert De Cooman

Imprecise probability models for inference in exponential families

Abstract

When considering sampling models described by a distribution from an exponential family, it is possible to create two types of imprecise probability models. One is based on the corresponding conjugate distribution and the other on the corresponding predictive distribution. In this paper, we show how these types of models can be constructed for any (regular, linear, canonical) exponential family, such as the centered normal distribution. To illustrate the possible use of such models, we take a look at credal classification. We show that they are very natural and potentially promising candidates for describing the attributes of a credal classifier, also in the case of continuous attributes.

Keywords. Exponential family, Imprecise probability models, Inference, Conjugate analysis, Naive credal classifier.

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Authors addresses:

Erik Quaeghebeur
Technologiepark-Zwijnaarde 914
9052 Zwijnaarde

Gert De Cooman
Technologiepark - Zwijnaarde 914
9052 Zwijnaarde

E-mail addresses:

Erik Quaeghebeur Erik.Quaeghebeur@UGent.be
Gert De Cooman gert.decooman@ugent.be


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