Probabilistic inference for dynamical systems

Sergio Davis, Diego González, Gonzalo Gutiérrez

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number696
JournalEntropy
Volume20
Issue number9
DOIs
Publication statusPublished - 12 Sep 2018

Keywords

  • Bayesian inference
  • Dynamical systems
  • Fluid equations

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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