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Softw."],"published-print":{"date-parts":[[2012,4]]},"abstract":"<jats:p>We employ recent work on computational noise to obtain near-optimal difference estimates of the derivative of a noisy function. Our analysis relies on a stochastic model of the noise without assuming a specific form of distribution. We use this model to derive theoretical bounds for the errors in the difference estimates and obtain an easily computable difference parameter that is provably near-optimal. Numerical results closely resemble the theory and show that we obtain accurate derivative estimates even when the noisy function is deterministic.<\/jats:p>","DOI":"10.1145\/2168773.2168777","type":"journal-article","created":{"date-parts":[[2012,5,7]],"date-time":"2012-05-07T18:47:42Z","timestamp":1336416462000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":40,"title":["Estimating Derivatives of Noisy Simulations"],"prefix":"10.1145","volume":"38","author":[{"given":"Jorge J.","family":"Mor\u00e9","sequence":"first","affiliation":[{"name":"Argonne National Laboratory"}]},{"given":"Stefan M.","family":"Wild","sequence":"additional","affiliation":[{"name":"Argonne National Laboratory"}]}],"member":"320","published-online":{"date-parts":[[2012,4]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Advances in Automatic Differentiation. 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