Quantifying predictability through information theory: small sample estimation in a non-Gaussian framework

DOI10.1016/j.jcp.2004.12.008zbMath1088.62502OpenAlexW2140357575MaRDI QIDQ1780633

Kyle Haven, Andrew J. Majda, Rafail V. Abramov

Publication date: 13 June 2005

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2004.12.008

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Dynamical systems in fluid mechanics, oceanography and meteorology (37N10) Meteorology and atmospheric physics (86A10) Statistical aspects of information-theoretic topics (62B10)

Related Items (17)

Challenges in Climate Science and Contemporary Applied Mathematics ⋮ Best probability density function for random sampled data ⋮ A maximum entropy method for particle filtering ⋮ Entropy measures for biological signal analyses ⋮ Filtering skill for turbulent signals for a suite of nonlinear and linear extended Kalman filters ⋮ Filtering nonlinear spatio-temporal chaos with autoregressive linear stochastic models ⋮ Mathematical strategies for filtering complex systems: Regularly spaced sparse observations ⋮ Information theory and dynamical system predictability ⋮ An improved algorithm for the multidimensional moment-constrained maximum entropy problem ⋮ New approximations and tests of linear fluctuation-response for chaotic nonlinear forced-dissipative dynamical systems ⋮ A homotopy training algorithm for fully connected neural networks ⋮ Quantifying dynamical predictability: the pseudo-ensemble approach ⋮ Mathematical test criteria for filtering complex systems: Plentiful observations ⋮ An equation-by-equation method for solving the multidimensional moment constrained maximum entropy problem ⋮ The multidimensional moment-constrained maximum entropy problem: A BFGS algorithm with constraint scaling ⋮ SENSITIVITY ANALYSIS OF NONLINEAR MODELS TO PARAMETER PERTURBATIONS FOR SMALL SIZE ENSEMBLES OF MODEL OUTPUTS ⋮ A practical computational framework for the multidimensional moment-constrained maximum entropy principle

Cites Work

• Unnamed Item
• Unnamed Item
• Statistical mechanics for truncations of the Burgers-Hopf equation: a model for intrinsic stochastic behavior with scaling
• A mathematical framework for quantifying predictability through relative entropy
• A practical computational framework for the multidimensional moment-constrained maximum entropy principle
• Weighing the Odds
• Information decay and the predictability of turbulent flows
• Moment-type estimation in the exponential family
• Remarkable statistical behavior for truncated Burgers–Hopf dynamics
• Quantifying Uncertainty for Non-Gaussian Ensembles in Complex Systems
• QUANTIFYING PREDICTABILITY IN A SIMPLE MODEL WITH COMPLEX FEATURES
• Hamiltonian structure and statistically relevant conserved quantities for the truncated Burgers‐Hopf equation
• Quantifying predictability in a model with statistical features of the atmosphere

This page was built for publication: Quantifying predictability through information theory: small sample estimation in a non-Gaussian framework