Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
Monte Carlo matrix trace estimation is a popular randomized technique to estimate the trace of implicitly-defined matrices via averaging quadratic forms across several observations of a random vector. The most common approach to analyze the quality of such estimators is to consider the variance over the total number of observations. In this paper we present a procedure to compute the variance of the estimator proposed in [W. Kong and G. Valiant, Spectrum estimation from samples, Ann. Statist. 45 2017, 5, 2218-2247] for the case of Gaussian random vectors and provide a sharper bound than previously available.
Heinz Koeppl, Marc Hafner, et al.
BMC Bioinformatics
Vladimir Yanovski, Israel A. Wagner, et al.
Ann. Math. Artif. Intell.
L Auslander, E Feig, et al.
Advances in Applied Mathematics
Michael Ray, Yves C. Martin
Proceedings of SPIE - The International Society for Optical Engineering