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{{Short description|Statistical method}}
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'''Random sample consensus''' ('''RANSAC''') is an [[iterative method]] to estimate parameters of a mathematical model from a set of observed data that contains [[outliers]], when outliers are to be {{clarify span|accorded no influence}} on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method.<ref>Data Fitting and Uncertainty, T. Strutz, Springer Vieweg (2nd edition, 2016).</ref> It is a non-deterministic algorithm in the sense that it produces a reasonable result only with a certain probability, with this probability increasing as more iterations are allowed. The algorithm was first published by Fischler and Bolles at [[SRI International]] in 1981. They used RANSAC to solve the
RANSAC uses [[Cross-validation (statistics)#Repeated random sub-sampling validation|repeated random sub-sampling]].<ref>{{cite web |last=Cantzler |first1=H. |title=Random Sample Consensus (RANSAC) |language=en |publisher=Institute for Perception, Action and Behaviour, Division of Informatics, University of Edinburgh |url=http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.3035&rep=rep1&type=pdf |archive-url=https://web.archive.org/web/20230204054340/http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.106.3035&rep=rep1&type=pdf |archive-date=2023-02-04 |url-status=dead}}</ref> A basic assumption is that the data consists of "inliers", i.e., data whose distribution can be explained by some set of model parameters, though may be subject to noise, and "outliers", which are data that do not fit the model. The outliers can come, for example, from extreme values of the noise or from erroneous measurements or incorrect hypotheses about the interpretation of data. RANSAC also assumes that, given a (usually small) set of inliers, there exists a procedure
==Example==
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