AI Article Synopsis
- Scientific Monte Carlo simulations are growing in size, making it essential to implement rigorous testing to ensure accurate and reproducible results.
- Traditional testing methods for deterministic algorithms do not effectively apply to stochastic algorithms used in Monte Carlo simulations.
- The paper proposes using statistical hypothesis testing as a reliable automated method for detecting problems in these simulations, advocating for its inclusion in standard testing procedures.
Article Abstract
The steadily increasing size of scientific Monte Carlo simulations and the desire for robust, correct, and reproducible results necessitates rigorous testing procedures for scientific simulations in order to detect numerical problems and programming bugs. However, the testing paradigms developed for deterministic algorithms have proven to be ill suited for stochastic algorithms. In this paper we demonstrate explicitly how the technique of statistical hypothesis testing, which is in wide use in other fields of science, can be used to devise automatic and reliable tests for Monte Carlo methods, and we show that these tests are able to detect some of the common problems encountered in stochastic scientific simulations. We argue that hypothesis testing should become part of the standard testing toolkit for scientific simulations.
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| http://dx.doi.org/10.1103/PhysRevE.96.053303 | DOI Listing |
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