The semantics of Subroutines and Iteration in the Bayesian Programming language ProBT

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.


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