LANS Publications
"Estimating Derivatives of Noisy Simulations"
J. J. More' and S. M. Wild
Preprint ANL/MCS-P1785-0810
Preprint Version: [pdf]
We employ recent work on computational noise to obtain near-optimal finite di fference estimates of the derivatives of a noisy function. Our analysis employs a stochastic model of the noise without assuming a specifi c form of distribution. We use this model to derive theoretical bounds for the errors in the diff erence estimates and obtain an easily computable di fference 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.