Preprint Article Version 1 This version is not peer-reviewed

Probabilistic Inference for Dynamical Systems

Version 1 : Received: 30 April 2018 / Approved: 2 May 2018 / Online: 2 May 2018 (11:58:32 CEST)

A peer-reviewed article of this Preprint also exists.

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, 696.

Journal reference: Entropy 2018, 20, 696
DOI: 10.3390/e20090696

Abstract

A 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 as fundamental the concept of a path, 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.

Subject Areas

path; inference; fluids; maximum entropy; maximum caliber

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