Quantifying Bayesian filter performance for turbulent dynamical systems through information theory

DOI10.4310/CMS.2014.V12.N5.A6zbMath1302.93212OpenAlexW1995003545MaRDI QIDQ482257

Michal Branicki, Andrew J. Majda

Publication date: 22 December 2014

Published in: Communications in Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4310/cms.2014.v12.n5.a6

zbMATH Keywords

Kalman filterinformation theoryuncertainty quantificationturbulent dynamical systems

Mathematics Subject Classification ID

Filtering in stochastic control theory (93E11) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Statistical turbulence modeling (76F55) Estimation and detection in stochastic control theory (93E10) Signal detection and filtering (aspects of stochastic processes) (60G35) Applications of stochastic analysis (to PDEs, etc.) (60H30) Asymptotic expansions of solutions to PDEs (35C20) Measures of information, entropy (94A17) Statistical aspects of information-theoretic topics (62B10)

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