Accession Number:

ADA605983

Title:

CHAMP: Changepoint Detection Using Approximate Model Parameters

Descriptive Note:

Corporate Author:

CARNEGIE-MELLON UNIV PITTSBURGH PA ROBOTICS INST

Report Date:

2014-06-01

Pagination or Media Count:

10.0

Abstract:

We introduce CHAMP, an algorithm for online Bayesian changepoint detection in settings where it is di cult or undesirable to integrate over the parameters of candidate models. Rather than requiring integration of the parameters of candidate models as in several other Bayesian approaches, we require only the ability to t model parameters to data segments. This approach greatly simpli es the use of Bayesian changepoint detection, allows it to be used with many more types of models, and improves performance when detecting parameter changes within a single model. Experimental analysis compares CHAMP to another state-of-the-art online Bayesian changepoint detection method.

Subject Categories:

  • Numerical Mathematics
  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE