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.

PPS_2017_Laurent_et_al_semantics_in_ProBT

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