Algorithmic information theory

Minimum message length

Minimum message length (MML) is a Bayesian information-theoretic method for statistical model comparison and selection. It provides a formal information theory restatement of Occam's Razor: even when models are equal in their measure of fit-accuracy to the observed data, the one generating the most concise explanation of data is more likely to be correct (where the explanation consists of the statement of the model, followed by the lossless encoding of the data using the stated model). MML was invented by Chris Wallace, first appearing in the seminal paper "An information measure for classification". MML is intended not just as a theoretical construct, but as a technique that may be deployed in practice. It differs from the related concept of Kolmogorov complexity in that it does not require use of a Turing-complete language to model data. (Wikipedia).

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From playlist Cryptography and Coding Theory

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Maximum and Minimum

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From playlist Real Numbers

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From playlist Statistics

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From playlist 241Fall13Ex3

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One Time Pad - Applied Cryptography

This video is part of an online course, Applied Cryptography. Check out the course here: https://www.udacity.com/course/cs387.

From playlist Applied Cryptography

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Finding the Minimum Number - Integer Word Problem

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From playlist PreAlgebra

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7. Viterbi decoding

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From playlist MIT 6.02 Introduction to EECS II: Digital Communication Systems, Fall 2012

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Stream archive: Recording videos for Axum course (2022-11-10)

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From playlist Seminar on Applied Geometry and Algebra (SIAM SAGA)

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Algebra - Minimum and Maximum (4 of 4)

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From playlist ALGEBRA 16 - FINDING MAXIMUM AND MINIMUM VALUES

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Lec 22 | MIT 6.451 Principles of Digital Communication II

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From playlist MIT 6.451 Principles of Digital Communication II

Related pages

Algorithmic probability | Mixture model | Minimum description length | A Mathematical Theory of Communication | Turing machine | Posterior probability | Kolmogorov complexity | Grammar induction | Turing completeness | Model selection | Conditional probability | Algorithmic information theory | Akaike information criterion | Bayesian network | Inductive probability | Occam's razor | Information theory | Fisher information