On Detecting Fake Coin Flip Sequences
Michael A. Kouritzin, Fraser Newton, Sterling Orsten, Daniel C. S. Wilson · Institute of Mathematical Statistics collections · 2008
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.