Probabilistic Inference for Dynamical Systems
AbstractA general framework for inference in dynamical systems is described, based on the language of Bayesian probability theory and making use of the maximum entropy principle. Taking the concept of a path as fundamental, the continuity equation and Cauchy’s equation for fluid dynamics arise naturally, while the specific information about the system can be included using the maximum caliber (or maximum path entropy) principle. View Full-Text
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Davis, S.; González, D.; Gutiérrez, G. Probabilistic Inference for Dynamical Systems. Entropy 2018, 20, 696.
Davis S, González D, Gutiérrez G. Probabilistic Inference for Dynamical Systems. Entropy. 2018; 20(9):696.Chicago/Turabian Style
Davis, Sergio; González, Diego; Gutiérrez, Gonzalo. 2018. "Probabilistic Inference for Dynamical Systems." Entropy 20, no. 9: 696.
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