A Survey of Probabilistic Models Using the Bayesian Programming Methodology as a Unifying Framework

This paper presents a survey of the most common probabilistic models for artefact conception. We use a generic formalism called Bayesian Programming, which we introduce briefly, for reviewing the main probabilistic models found in the literature. Indeed, we show that Bayesian Networks, Markov Localization, Kalman filters, etc., can all be captured under this single formalism. We believe it offers the novice reader a good introduction to these models, while still providing the experience reader an enriching global view of the field.

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