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 ...

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 circumference of a sphere is measured to be 24 cm, with a possible error of 0.25 cm. Use the differential \(dV\) to estimate the maximum error in the calculated ...

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 ...

The Annals of Applied Probability, Vol. 12, No. 1 (Feb., 2002), pp. 196-223 (28 pages) In mathematical finance, the price of the so-called "American Put option" is given by the value function of the ...

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 ...

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. Pollsters trying to predict presidential election results ...

A critical issue in the credit risk industry is the accurate, efficient and robust pricing of collateralized debt obligations (CDOs) in a variety of mathematical models. These and many similar basket ...