Strict MML (SMML)

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Strict Minimum Message Length (SMML) inference (Wallace & Boulton 1975) constructs a mapping from the data space to the set of models (parameters) so as to minimise the expected length of a two-part message:  `model; (data|model)'. Note that the mapping defines a partition of the data space, each part being the data values that map to a particular model (parameter value).

SMML is invariant and consistent, and handles model selection, parameter estimation and hypothesis testing. Unfortunately SMML inference is NP-hard for most problems, although a polynomial-time algorithm exists for the Binomial distribution (Farr & Wallace 1997, 2002). Fortunately, MML (Wallace & Boulton 1968, Wallace & Freeman 1987) is a feasible approximation to SMML.

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© L. Allison   (or as otherwise indicated),
Faculty of Information Technology (Clayton), Monash University, Australia 3800 (6/'05 was School of Computer Science and Software Engineering, Fac. Info. Tech., Monash University,
was Department of Computer Science, Fac. Comp. & Info. Tech., '89 was Department of Computer Science, Fac. Sci., '68-'71 was Department of Information Science, Fac. Sci.)
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