Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression

DOI10.1080/10629360600569279zbMath1122.62074OpenAlexW2008389020WikidataQ126268161 ScholiaQ126268161MaRDI QIDQ3446978

W. E. Leithead, Yu-Nong Zhang

Publication date: 27 June 2007

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10629360600569279

zbMATH Keywords

compensationpower-series expansionlogarithm of matrix determinant

Mathematics Subject Classification ID

Related Items (8)

A randomized algorithm for approximating the log determinant of a symmetric positive definite matrix ⋮ On randomized trace estimates for indefinite matrices with an application to determinants ⋮ Fast Estimation of $tr(f(A))$ via Stochastic Lanczos Quadrature ⋮ Approximating Spectral Sums of Large-Scale Matrices using Stochastic Chebyshev Approximations ⋮ Variational Gaussian approximation for Poisson data ⋮ Log-det approximation based on uniformly distributed seeds and its application to Gaussian process regression ⋮ Randomized block Krylov subspace methods for trace and log-determinant estimators ⋮ Unnamed Item

Cites Work

• Unnamed Item
• Unnamed Item
• Chebyshev approximation of log-determinants of spatial weight matrices
• Monte Carlo estimates of the log determinant of large sparse matrices
• Extreme eigenfunctions of adjacency matrices for planar graphs employed in spatial analyses
• Approximations to the determinant term in gaussian maximum likelihood estimation of some spatial models
• Trust Region Methods
• Bayesian regression and classification using mixtures of Gaussian processes
• Faster maximum likelihood estimation of very large spatial autoregressive models: an extension of the Smirnov–Anselin result
• 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.
• Recurrent neural networks for nonlinear output regulation

This page was built for publication: Approximate implementation of the logarithm of the matrix determinant in Gaussian process regression