Applies uncertainty quantification and Chernoff-like bounds to quantify and certify variability in flexible protein-protein docking algorithms.
Quasi-Monte Carlo, Discrepancies and Error Estimates
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abstract
We discuss the problem of defining an estimate for the error in quasi-Monte Carlo integration. The key issue is the definition of an ensemble of quasi-random point sets that, on the one hand, includes a sufficiency of equivalent point sets, and on the other hand uses information on the degree of uniformity of the point set actually used, in the form of a discrepancy or diaphony. A few examples of such discrepancies are given. We derive the distribution of our error estimate in the limit of large number of points. In many cases, Gaussian central limits are obtained. We also present numerical results for the quadratic star-discrepancy for a number of quasi-random sequences.
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q-bio.BM 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Quantified uncertainty of flexible protein-protein docking algorithms
Applies uncertainty quantification and Chernoff-like bounds to quantify and certify variability in flexible protein-protein docking algorithms.