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Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available.
Edwin T. Jaynes proposed that probability could be considered as an alternative and an extension of logic for rational reasoning with incomplete and uncertain information. In his founding book Probability Theory: The Logic of Science[1] he developed this theory and proposed what he called “the robot,” which was not
a physical device, but an inference engine to automate probabilistic reasoning—a kind of Prolog for probability instead of logic. Bayesian programming[2] is a formal and concrete implementation of this "robot".
Bayesian programming may also be seen as an algebraic formalism to specify graphical models such as, for instance, Bayesian networks, dynamic Bayesian networks, Kalman filters or hidden Markov models. Indeed, Bayesian Programming is more general than Bayesian networks and has a power of expression equivalent to probabilistic factor graphs.[3]
^Jaynes, E. T. (10 April 2003). Probability Theory: The Logic of Science. Cambridge University Press. ISBN 978-1-139-43516-1.
^Bessiere, Pierre; Mazer, Emmanuel; Manuel Ahuactzin, Juan; Mekhnacha, Kamel (20 December 2013). Bayesian Programming. CRC Press. ISBN 978-1-4398-8032-6.
^"Expression Graphs: Unifying Factor Graphs and Sum-Product Networks" (PDF). bcf.usc.edu.
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