On Detecting Fake Coin Flip Sequences

• Author(s) / Creator(s)

  • Kouritzin, Michael
  • Newton, Fraser
  • Orsten, Sterling
  • Wilson, Daniel

• 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.

• Date created

  2008

• Subjects / Keywords

  • Algorithms
  • Covariance process
  • Probability
  • Fake coin experimentation
  • Filtering

• Type of Item

  Conference Paper

• DOI

  https://doi.org/10.7939/R3GH9BG3N

• License

  © 2008. Institute of Mathematical Statistics. This version of this article is open access and can be downloaded and shared. The original author(s) and source must be cited.