Abstract: Stochastic approximation methods have been extensively studied in the literature for solving systems of stochastic equations and stochastic optimization problems where function values and ...

Adequate mathematical modeling is the key to success for many real-world projects in engineering, medicine, and other applied areas. Once a well-suited model is established, it can be thoroughly ...

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 ...

Abstract: As computing devices continue growing explosively, computational efficiency is increasingly important. To improve the efficiency of computations, approximate computing is widely used in ...

Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA Characterizing the spiking statistics of neurons receiving noisy synaptic input is a central problem ...

A wealth of computationally efficient approximation methods for Gaussian process regression have been recently proposed. We give a unifying overview of sparse approximations, following ...

Inverse problems in statistical physics are motivated by the challenges of “big data” in different fields, in particular experiments in biology. In inverse problems, the usual procedure of statistical ...