Subscribe to DSC Newsletter

Bayesian Outlier Detection with Dirichlet Process Mixtures

Matthew S. Shotwell and Elizabeth H. Slate

Abstract.

We introduce a Bayesian inference mechanism for outlier detection using the augmented Dirichlet process mixture. Outliers are detected by forming a maximum a posteriori (MAP) estimate of the data partition. Observations that comprise small or singleton clusters in the estimated partition are considered outliers. We offer a novel interpretation of the Dirichlet process precision parameter, and demonstrate its utility in outlier detection problems. The precision parameter is used to form an outlier detection criterion based on the Bayes factor for an
outlier partition versus a class of partitions with fewer or no outliers. We further introduce a computational method for MAP estimation that is free of posterior sampling, and guaranteed to find a MAP estimate in finite time. The novel methods are compared with several established strategies in a yeast microarray time
series.

http://ba.stat.cmu.edu/journal/2011/vol06/issue04/shotwell.pdf

Views: 1253

Tags: asymptotix

Comment

You need to be a member of AnalyticBridge to add comments!

Join AnalyticBridge

Comment by Dario H. Romero on March 3, 2017 at 9:36am

It seems the link to the pdf document is reaching a non-existing document

On Data Science Central

© 2019   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC   Powered by

Badges  |  Report an Issue  |  Privacy Policy  |  Terms of Service