Quasi-Monte Carlo
A guide to quasi-Monte Carlo sampling, discrepancy, scrambling and practical use of sampling engines.
External resource. No execution or independent verification is claimed here.
How does scrambled low-discrepancy sampling compare with independent random sampling on bounded smooth and discontinuous integrands?
The evaluator owns the integrands and exact integrals, varies independent randomizations and reports empirical error with its uncertainty.
What you could produce
- A versioned minimal protocol, evaluator-owned test cases, and a comparison report with numerical/statistical uncertainty and failures.
Before you use it
- SciPy 1.18.0
- Compatible NumPy and native numerical-library build; exact environment not prepared
Limits to keep in view
- No upstream example, source package, build hook or submitted code was executed.
- The current qualified pilot accepts only self-contained Python 3.13 with a 90-second deadline. This reference's package/runtime is not qualified for that path.
- The proposed protocol requires bounded resource estimates and an independently controlled evaluator before any scientific execution claim.
Source and permission context
SciPy documentation. Quasi-Monte Carlo. https://docs.scipy.org/doc/scipy-1.18.0/tutorial/stats/quasi_monte_carlo.html; observed version 1.18.0.
Catalog listing reviewed. This review covers the description and source links displayed here.
Versioned SciPy 1.18.0 page and BSD-3-Clause documentation evidence reviewed.
Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.
documentation · BSD-3-Clause
SciPy's versioned developer policy explicitly includes documentation under its default BSD license, subject to separately specified exceptions; the matching 1.18.0 LICENSE.txt identifies the three-clause terms.
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- Third-party figures, linked papers, datasets and dependency licenses have not been assessed; no external content is copied into this catalog.