Minimum message length (MML) is a Bayesian information-theoretic method for statistical model comparison and selection.
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In this paper, we consider a range of criteria which may be applied to determine if some data should be segmented into two or regions. We develop a information ...
The segmentation problem is to decide whether to divide a segment into one or more sub-segments and to choose where to make the divisions. The segmentation ...
The segmentation problem arises wherever it is desired to partition data into distinct homogeneous segments (or regions). The segmentation problem is to decide ...
We develop a information theoretic criterion (MML) for the segmentation of univariate data with Gaussian errors. We perform simulations comparing segmentation ...
Definition. Minimum message length (MML) is a theory of inductive inference whereby the preferred model is the one minimizing the expected message length ...
MML is a formal information theory restatement of Occam's Razor: even when models are not equal in goodness of fit accuracy to the observed data.
the Minimum Message Length principle. MML thus requires the construction of a message, the length of which determines an objective function called a message.
MML is a Bayesian method of inference: for hypothesis H, data D, event E. MML is a practical realisation [All05, All18] of Ockham's razor.
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