Change-Point Estimation Using New Minimum Message Length Approximations

Leigh J. Fitzgibbon, David L. Dowe, and Lloyd Allison,
School of Computer Science and Software Engineering,
Monash University, Clayton, VIC 3168, Australia.

Appears in the Proc. Seventh Pacific Rim International Conference on Artificial Intelligence, August 18-22, 2002, National Center of Sciences, Tokyo, Japan [www]

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Abstract. This paper investigates the coding of change-points in the information-theoretic Minimum Message Length (MML) framework. Change-point coding regions affect model selection and parameter estimation in problems such as time series segmentation and decision trees. The Minimum Message Length (MML) and Minimum Description Length (MDL78) approaches to change-point problems have been shown to perform well by several authors. In this paper we compare some published MML and MDL78 methods and introduce some new MML approximations called `MMLDc' and `MMLDF'. These new approximations are empirically compared with Strict MML (SMML), Fairly Strict MML (FSMML), MML68, the Minimum Expected Kullback-Leibler Distance (MEKLD) loss function and MDL78 on a tractable binomial change-point problem.


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