Approximation theory and asymptotic methods form a foundational framework that bridges classical ideas with modern numerical analysis, enabling researchers to obtain practical, near‐optimal solutions ...

In this talk we present few instances of multilevel approximation methods involving PDEs with random parameters and associated scalar output quantities of interest (QoI). Multilevel methods aim at ...

Stein's method has emerged as a powerful and versatile tool in probability theory for deriving error bounds in distributional approximations. Originally developed to ...

The object of this paper is a theoretical study of the convergence of approximation methods (Galerkin and finite difference methods) to compute eigenelements of a closed linear operator T in a Banach ...

This is a preview. Log in through your library . Abstract Practical use of Bayesian methods usually involves obtaining certain characteristics of the posterior distribution of the parameter of ...

Covers asymptotic evaluation of integrals (stationary phase and steepest descent), perturbation methods (regular and singular methods, and inner and outer expansions), multiple scale methods, and ...

Clinical trials have traditionally followed a fixed design, in which patient allocation to treatments is fixed throughout the trial and specified in the protocol. The primary goal of this static ...

This paper develops a new scheme for improving an approximation method of a probability density function, which is inspired by the idea in the Hilbert space projection theorem. Moreover, we apply ...

In this paper we evaluate the single-loss approximation method for high-quantile loss estimation on the basis of SAS OpRisk Global Data. Due to its simplicity, the single-loss approximation method has ...