Probabilistic inference for dynamical systems

dc.contributor.author	Davis, S.
dc.contributor.author	González, D.
dc.contributor.author	Gutiérrez, G.
dc.date.accessioned	2019-12-10T12:46:54Z
dc.date.available	2019-12-10T12:46:54Z
dc.date.issued	2018-09
dc.description	Indexación: Scopus.	es
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. © 2018 by the authors.	es
dc.description.uri	https://www.mdpi.com/1099-4300/20/9/696
dc.identifier.citation	Entropy, 20(9), art. no. 696.	es
dc.identifier.issn	1099-4300
dc.identifier.other	DOI: 10.3390/e20090696
dc.identifier.uri	http://repositorio.unab.cl/xmlui/handle/ria/11304
dc.language.iso	en	es
dc.publisher	MDPI AG	es
dc.subject	Bayesian inference	es
dc.subject	Dynamical systems	es
dc.subject	Fluid equations	es
dc.title	Probabilistic inference for dynamical systems	es
dc.type	Artículo	es

Mostrando 1 - 1 de 1

Mostrando 1 - 1 de 1