Log-det approximation based on uniformly distributed seeds and its application to Gaussian process regression

DOI10.1016/j.cam.2007.08.012zbMath1146.65015OpenAlexW1982537449MaRDI QIDQ939524

L. Walshe, W. E. Leithead, Yu-Nong Zhang, Douglas J. Leith

Publication date: 22 August 2008

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2007.08.012

zbMATH Keywords

numerical examplesGaussian random seedslog-det approximationO\((N^{2})\) operationsrandomized trace estimatoruniformly distributed seeds

Mathematics Subject Classification ID

Related Items (6)

A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix ⋮ Randomized matrix-free trace and log-determinant estimators ⋮ Zhang neural network for online solution of time-varying convex quadratic program subject to time-varying linear-equality constraints ⋮ A general scheme for log-determinant computation of matrices via stochastic polynomial approximation ⋮ Randomized block Krylov subspace methods for trace and log-determinant estimators ⋮ Nonintrusive approximation of parametrized limits of matrix power algorithms – application to matrix inverses and log-determinants

Cites Work

• Monte Carlo estimates of the log determinant of large sparse matrices
• Approximations to the determinant term in gaussian maximum likelihood estimation of some spatial models
• Maximum likelihood estimation of models for residual covariance in spatial regression
• Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression
• Computational Methods for Inverse Problems
• A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
• Fast maximum likelihood estimation of very large spatial autoregressive models: a characteristic polynomial approach.
• Unnamed Item

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