The blog Why Add Q to MC?, explained the advantages of carefully chosen, low discrepancy sampling sites for approximating multivariate integrals, or equivalently, expectations of functions of multivariate random variables \begin{align} \mu: &= \int_{[0,1]^d} f(\boldsymbol{x}) \, \text{d}{\boldsymbol{x}} = {\mathbb{E}}[f(\boldsymbol{x})], \; \text{where } \boldsymbol{x} \sim \mathcal{U}[0,1]^d, \\ \mu \approx \widehat{\mu}_n &: = \frac 1n \sum_{i=1}^n f(\boldsymbol{x}_i),Continue reading “Bayesian Stopping Criteria”

# Jagadeeswaran Rathinavel

## Quasi-Monte Carlo Software Article

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”

## Speeding up QMCPy with Distributable C Code

Many Python packages rely on underlying C or C++ code to speed up their numerical methods. For example, NumPy calls C and C++ extensions in order to speed up matrix manipulation algorithms. Real Python’s article Python Bindings: Calling C or C++ From Python discusses a few reasons why you may want to utilize C or C++Continue reading “Speeding up QMCPy with Distributable C Code”

## 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”