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Probabilistic Inference for Dynamical Systems

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, wh...

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Detalles Bibliográficos
Autores principales: Davis, Sergio, González, Diego, Gutiérrez, Gonzalo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513225/
https://www.ncbi.nlm.nih.gov/pubmed/33265785
http://dx.doi.org/10.3390/e20090696
Descripción
Sumario: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.