A Bayesian nonparametric approach to reconstruction and prediction of random dynamical systems

DOI10.1063/1.4990547zbMath1388.37065arXiv1511.00154OpenAlexW2732728733WikidataQ38692614 ScholiaQ38692614MaRDI QIDQ4644231

Konstantinos Kaloudis, Spyridon J. Hatjispyros, Christos Merkatas

Publication date: 30 May 2018

Published in: Chaos: An Interdisciplinary Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1511.00154

zbMATH Keywords

time series dataMarkov chain, Monte Carlo method

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05) Bayesian inference (62F15) Random measures (60G57) Generation, random and stochastic difference and differential equations (37H10) Time series analysis of dynamical systems (37M10)

Related Items (6)

Reconstructing latent dynamical noise for better forecasting observables ⋮ Inference for differential equation models using relaxation via dynamical systems ⋮ A Bayesian nonparametric approach to dynamical noise reduction ⋮ System identification using autoregressive Bayesian neural networks with nonparametric noise models ⋮ Joint reconstruction and prediction\break of random dynamical systems under\break borrowing of strength ⋮ A Bayesian nonparametric approach to the approximation of the global stable manifold

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