We introduce an algorithm that learns gradients from samples in the supervised learning framework. An error analysis is given for the convergence of the gradient estimated by the algorithm to the true gradient. The utility of the algorithm for the problem of variable selection as well as determining variable covariance is illustrated on simulated data as well as two gene expression datasets. Fo...

When applied to multimass velocity-map imaging data, covariance analysis reveals correlations between different fragment ions formed from the same parent molecule and can provide detailed insights into fragmentation dynamics. Covariances time-of-flight signals for two show that they are in event, while covariances their images, often referred as “recoil-frame covariances”, reveal details of cor...

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process covariances initial measurement covariance, a method fixed-point iteration are utilized to jointly estimate the posterior vector under stochastic mechanism. The proposed algorithm ensures low communication loads...

Summary We conduct a study of the aliased spectral densities Matérn covariance functions on regular grid points, elucidating properties popular approximation based stochastic partial differential equations. While other researchers have shown that this can work well for function, we find it assigns too much power at high frequencies and does not provide increasingly accurate approximations to in...

We study weakest precondition reasoning about the (co)variance of outcomes and the variance of run–times of probabilistic programs with conditioning. For outcomes, we show that approximating (co)variances is computationally more difficult than approximating expected values. In particular, we prove that computing both lower and upper bounds for (co)variances is Σ 2–complete. As a consequence, ne...