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Entropy 2018, 20(9), 696; https://doi.org/10.3390/e20090696

Probabilistic Inference for Dynamical Systems

1
Comisión Chilena de Energía Nuclear, Casilla 188-D Santiago, Chile
2
Departamento de Física, Facultad de Ciencias Exactas, Universidad Andres Bello, Sazié 2212, Piso 7, 8370136 Santiago, Chile
3
Grupo de Nanomateriales, Departamento de Física, Facultad de Ciencias, Universidad de Chile, Casilla 653 Santiago, Chile
*
Author to whom correspondence should be addressed.
Received: 30 April 2018 / Revised: 5 September 2018 / Accepted: 6 September 2018 / Published: 12 September 2018
Full-Text   |   PDF [257 KB, uploaded 12 September 2018]

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 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
Keywords: dynamical systems; bayesian inference; fluid equations dynamical systems; bayesian inference; fluid equations
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Davis, S.; González, D.; Gutiérrez, G. Probabilistic Inference for Dynamical Systems. Entropy 2018, 20, 696.

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