A Krylov subspace method for covariance approximation and simulation of random processes and fields
From MaRDI portal
Publication:1398035
DOI10.1023/A:1023530718764zbMath1056.93020OpenAlexW1561428091MaRDI QIDQ1398035
Alan S. Willsky, Michael K. Schneider
Publication date: 6 August 2003
Published in: Multidimensional Systems and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1023/a:1023530718764
simulationmatrix square rootconjugate gradientKrylov subspaceLanczoscovariance matrix approximationsample
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (10)
Best approximation of the identity mapping: The case of variable finite memory ⋮ Randomized spectral and Fourier-wavelet methods for multidimensional Gaussian random vector fields ⋮ Iterative numerical methods for sampling from high dimensional Gaussian distributions ⋮ Sparsified randomization algorithms for low rank approximations and applications to integral equations and inhomogeneous random field simulation ⋮ Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems ⋮ Krylov space approximate Kalman filtering ⋮ Optimal multilinear estimation of a random vector under constraints of causality and limited memory ⋮ Toward Best Approximation of Nonlinear Systems: A Case of Models with Memory ⋮ Polynomial Accelerated Solutions to a Large Gaussian Model for Imaging Biofilms: In Theory and Finite Precision ⋮ High-Dimensional Gaussian Sampling: A Review and a Unifying Approach Based on a Stochastic Proximal Point Algorithm
This page was built for publication: A Krylov subspace method for covariance approximation and simulation of random processes and fields