Next Article in Journal
Strange Attractors Generated by Multiple-Valued Static Memory Cell with Polynomial Approximation of Resonant Tunneling Diodes
Previous Article in Journal
Optimization of Thurston’s Core Entropy Algorithm for Polynomials with a Critical Point of Maximal Order
Previous Article in Special Issue
Microcanonical Entropy, Partitions of a Natural Number into Squares and the Bose–Einstein Gas in a Box
Open AccessArticle

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.
Entropy 2018, 20(9), 696; https://doi.org/10.3390/e20090696
Received: 30 April 2018 / Revised: 5 September 2018 / Accepted: 6 September 2018 / Published: 12 September 2018
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
MDPI and ACS Style

Davis, S.; González, D.; Gutiérrez, G. Probabilistic Inference for Dynamical Systems. Entropy 2018, 20, 696.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop