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Stochastic dynamical modeling of turbulent flows
Advanced measurement techniques and high-performance computing have made large
data sets available for a range of turbulent flows in engineering applications. Drawing on …
data sets available for a range of turbulent flows in engineering applications. Drawing on …
[ספר][B] Errors-in-variables methods in system identification
T Söderström - 2018 - books.google.com
This book presents an overview of the different errors-in-variables (EIV) methods that can be
used for system identification. Readers will explore the properties of an EIV problem. Such …
used for system identification. Readers will explore the properties of an EIV problem. Such …
Proximal algorithms for large-scale statistical modeling and sensor/actuator selection
Several problems in modeling and control of stochastically driven dynamical systems can be
cast as regularized semidefinite programs. We examine two such representative problems …
cast as regularized semidefinite programs. We examine two such representative problems …
A new family of high-resolution multivariate spectral estimators
In this paper, we extend the Beta divergence family to multivariate power spectral densities.
Similarly to the scalar case, we show that it smoothly connects the multivariate Kullback …
Similarly to the scalar case, we show that it smoothly connects the multivariate Kullback …
Multivariate spectral estimation based on the concept of optimal prediction
In this technical note, we deal with a spectrum approximation problem arising in THREE-like
multivariate spectral estimation approaches. The solution to the problem minimizes a …
multivariate spectral estimation approaches. The solution to the problem minimizes a …
Identification of sparse reciprocal graphical models
In this letter we propose an identification procedure of a sparse graphical model associated
to a Gaussian stationary stochastic process. The identification paradigm exploits the …
to a Gaussian stationary stochastic process. The identification paradigm exploits the …
Variance analysis of covariance and spectral estimates for mixed-spectrum continuous-time signals
The estimation of the covariance function of a stochastic process, or signal, is of integral
importance for a multitude of signal processing applications. In this work, we derive closed …
importance for a multitude of signal processing applications. In this work, we derive closed …
Rational approximations of spectral densities based on the Alpha divergence
We approximate a given rational spectral density by one that is consistent with prescribed
second-order statistics. Such an approximation is obtained by selecting the spectral density …
second-order statistics. Such an approximation is obtained by selecting the spectral density …
An interpretation of the dual problem of the THREE-like approaches
Spectral estimation can be performed using the so called THREE-like approach. Such
method leads to a convex optimization problem whose solution is characterized through its …
method leads to a convex optimization problem whose solution is characterized through its …
Robust identification of “sparse plus low-rank” graphical models: An optimization approach
Motivated by graphical models, we consider the “Sparse Plus Low-rank” decomposition of a
positive definite concentration matrix-the inverse of the covariance matrix. This is a classical …
positive definite concentration matrix-the inverse of the covariance matrix. This is a classical …