Authors: R. Laurent, K. Mekhnacha, E. Mazer and P. Bessière
Abstract: Bayesian models are tools of choice when solving problems with incomplete information. Bayesian networks provide a first but limited approach to address such problems. For real world applications, additional semantics is needed to construct more complex models, especially those with repetitive structures or substructures. ProBT, a Bayesian a programming language, provides a set of constructs for developing and applying complex models with substructures and repetitive structures.
The goal of this paper is to present and discuss the semantics associated to these constructs.