On Detecting Fake Coin Flip Sequences
Michael A. Kouritzin, Fraser Newton, Sterling Orsten, Daniel C. Wilson
Abstract
Classification of data as true or fabricated has applications in fraud detection and verification of data samples. In this paper, we apply nonlinear filtering to a simplified fraud-detection problem: classifying coin flip sequences as either real or faked. On the way, we propose a method for generating Bernoulli variables with given marginal probabilities and pair-wise covariances. Finally, we present the empirical performance of the classification algorithm.
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Permanent link to this document: http://projecteuclid.org/euclid.imsc/1233152938
Digital Object Identifier: doi:10.1214/074921708000000336
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