We recently uploaded an article on Quasi-Monte Carlo Software to https://arxiv.org/abs/2102.07833. Abstract: Practitioners wishing to experience the efficiency gains from using low discrepancy sequences need correct, well-written software. This article, based on our MCQMC 2020 tutorial, describes some of the better quasi-Monte Carlo (QMC) software available. We highlight the key software components required to approximateContinue reading “Quasi-Monte Carlo Software Article”
Michael McCourt
QMCPy Version 1.0
The developers of QMCPy are happy to announce the release of version 1.0 on February 12, 2021, Chinese New Year! We would like to thank all those who have made this development possible. A special thank you to Developers: Sou-Cheng T. Choi, Fred J. Hickernell, Michael McCourt, Jagadeeswaran Rathinavel, and Aleksei Sorokin; Collaborators: Mike Giles,Continue reading “QMCPy Version 1.0”
qEI with QMCPy
Quasi-Monte Carlo methods (QMC) are a valuable tool for sampling random variables in a structured fashion; this allows for computing key statistics of random variables more efficiently than with i.i.d. sampling. Such quantities can play fundamental roles in larger algorithms, making their efficient computation fundamental to practical implementations of numerous applications. This was the motivationContinue reading “qEI with QMCPy”