Sphere-packings, lattices, and groups
Sphere-packings, lattices, and groups
MML Markov classification of sequential data
Statistics and Computing
MML clustering of multi-state, Poisson, vonMises circular and Gaussian distributions
Statistics and Computing
Finding Cutpoints in Noisy Binary Sequences - A Revised Empirical Evaluation
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Point Estimation Using the Kullback-Leibler Loss Function and MML
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
Minimum Message Length Segmentation
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
The Kindest Cut: Minimum Message Length Segmentation
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
MML Estimation of the Parameters of the Sherical Fisher Distribution
ALT '96 Proceedings of the 7th International Workshop on Algorithmic Learning Theory
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Change-Point Estimation Using New Minimum Message Length Approximations
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
MML mixture models of heterogeneous poisson processes with uniform outliers for bridge deterioration
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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Explicit segmentation is the partitioning of data into homogeneous regions by specifying cut-points. W. D. Fisher (1958) gave an early example of explicit segmentation based on the minimisation of squared error. Fisher called this the grouping problem and came up with a polynomial time Dynamic Programming Algorithm (DPA). Oliver, Baxter and colleagues (1996, 1997, 1998) have applied the information-theoretic Minimum Message Length (MML) principle to explicit segmentation. They have derived formulas for specifying cut-points imprecisely and have empirically shown their criterion to be superior to other segmentation methods (AIC, MDL and BIC). We use a simple MML criterion and Fisher's DPA to perform numerical Bayesian (summing and) integration (using message lengths) over the cut-point location parameters. This gives an estimate of the number of segments, which we then use to estimate the cut-point positions and segment parameters by minimising the MML criterion. This is shown to have lower Kullback-Leibler distances on generated data.