This is a decommissioned version of ERA which is running to enable completion of migration processes. All new collections and items and all edits to existing items should go to our new ERA instance at https://ualberta.scholaris.ca - Please contact us at erahelp@ualberta.ca for assistance!
- 573 views
- 1406 downloads
On Detecting Fake Coin Flip Sequences
-
- Author(s) / Creator(s)
-
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
-
- Type of Item
- Conference/Workshop Presentation
-
- 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.