Probabilistic inference for dynamical systems

Author	dc.contributor.author	Davis, Sergio
Author	dc.contributor.author	González, Diego
Author	dc.contributor.author	Gutiérrez, Gonzalo
Admission date	dc.date.accessioned	2019-05-31T15:21:10Z
Available date	dc.date.available	2019-05-31T15:21:10Z
Publication date	dc.date.issued	2018
Cita de ítem	dc.identifier.citation	Entropy, Volumen 20, Issue 9, 2018
Identifier	dc.identifier.issn	10994300
Identifier	dc.identifier.other	10.3390/e20090696
Identifier	dc.identifier.uri	https://repositorio.uchile.cl/handle/2250/169521
Abstract	dc.description.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.
Lenguage	dc.language.iso	en
Publisher	dc.publisher	MDPI AG
Type of license	dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Chile
Link to License	dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/
Source	dc.source	Entropy
Keywords	dc.subject	Bayesian inference
Keywords	dc.subject	Dynamical systems
Keywords	dc.subject	Fluid equations
Título	dc.title	Probabilistic inference for dynamical systems
Document type	dc.type	Artículo de revista
Cataloguer	uchile.catalogador	jmm
Indexation	uchile.index	Artículo de publicación SCOPUS
uchile.cosecha	uchile.cosecha	SI

Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile