Abstract
(Quasi-)Monte Carlo, (Q)MC, methods are a class of powerful numerical integration algorithms that have been proven to scale well to high dimensions. Various techniques exist to decrease the computational cost of (Q)MC methods. This article focuses on importance sampling, a technique that performs variable transformations to make the integral easier for (Q)MC approximation. The build up to composed importance sampling is paralleled by code from our QMCPy package that implements these concepts.
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