Information theory, model error, and predictive skill of stochastic models for complex nonlinear systems
From MaRDI portal
Publication:1926280
DOI10.1016/j.physd.2012.07.005zbMath1260.62067OpenAlexW2114340042MaRDI QIDQ1926280
Andrew J. Majda, Dimitrios Giannakis, Illia Horenko
Publication date: 28 December 2012
Published in: Physica D (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.physd.2012.07.005
Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Information theory (general) (94A15) Statistical aspects of information-theoretic topics (62B10)
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Cites Work
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- Flow transitions resembling bifurcations of the logistic map in simulations of the baroclinic rotating annulus
- Linear response theory for statistical ensembles in complex systems with time-periodic forcing
- Bayesian model averaging: A tutorial. (with comments and a rejoinder).
- Identification of almost invariant aggregates in reversible nearly uncoupled Markov chains
- Coarse-grained stochastic processes and Monte Carlo simulations in lattice systems.
- Analysis of persistent nonstationary time series and applications
- Generator estimation of Markov jump processes
- Fitting timeseries by continuous-time Markov chains: a quadratic programming approach
- On the nature of turbulence
- A mathematical framework for quantifying predictability through relative entropy
- On Analysis of Nonstationary Categorical Data Time Series: Dynamical Dimension Reduction, Model Selection, and Applications To Computational Sociology
- Normal forms for reduced stochastic climate models
- High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability
- Finite Element Approach to Clustering of Multidimensional Time Series
- On Metastable Conformational Analysis of Nonequilibrium Biomolecular Time Series
- Information flow between subspaces of complex dynamical systems
- Relative Entropy Measures of Multivariate Dependence
- Improving model fidelity and sensitivity for complex systems through empirical information theory
- Probabilistic evaluation of time series models: A comparison of several approaches
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Distinct metastable atmospheric regimes despite nearly Gaussian statistics: A paradigm model
- An Adaptive Markov Chain Monte Carlo Approach to Time Series Clustering of Processes with Regime Transition Behavior
- Error Analysis of Coarse‐Graining for Stochastic Lattice Dynamics
- Elements of Information Theory
