12 citations · 22 across the 12 of their papers we have counts for
5 papers · 1 filter
Optimal Monte Carlo integration on matrix manifolds
Aleksei G. Sorokin, Yihan Guo, Lek-Heng Lim
This article develops optimal order (up to logarithmic factors) weighted cubature rules for Monte Carlo numerical integration in Sobolev reproducing kernel Hilbert spaces over comm…
Empirical Bernstein and betting confidence intervals for randomized quasi-Monte Carlo
Aadit Jain, Fred J. Hickernell, Art B. Owen +1
Randomized quasi-Monte Carlo (RQMC) methods estimate the mean of a random variable by sampling an integrand at equidistributed points. For scrambled digital nets, the resulting…
Quasi-Monte Carlo Methods: What, Why, and How?
Fred J. Hickernell, Nathan Kirk, Aleksei G. Sorokin
Many questions in quantitative finance, uncertainty quantification, and other disciplines are answered by computing the population mean, , where instances of $Y:…
On Bounding and Approximating Functions of Multiple Expectations using Quasi-Monte Carlo
Aleksei G. Sorokin, Jagadeeswaran Rathinavel
Monte Carlo and Quasi-Monte Carlo methods present a convenient approach for approximating the expected value of a random variable. Algorithms exist to adaptively sample the random…
Challenges in Developing Great Quasi-Monte Carlo Software
Sou-Cheng T. Choi, Yuhan Ding, Fred J. Hickernell +2
Quasi-Monte Carlo (QMC) methods have developed over several decades. With the explosion in computational science, there is a need for great software that implements QMC algorithms.…