By Peter D. Grunwald, In Jae Myung, Mark A. Pitt

ISBN-10: 0262072629

ISBN-13: 9780262072625

ISBN-10: 1423729447

ISBN-13: 9781423729440

The method of inductive inference -- to deduce normal legislation and rules from specific situations -- is the foundation of statistical modeling, trend popularity, and laptop studying. The minimal Descriptive size (MDL) precept, a robust approach to inductive inference, holds that the simplest rationalization, given a restricted set of saw info, is the person who allows the maximum compression of the information -- that the extra we will compress the information, the extra we find out about the regularities underlying the knowledge. Advances in minimal Description size is a sourcebook that would introduce the medical neighborhood to the principles of MDL, contemporary theoretical advances, and sensible applications.The ebook starts with an intensive educational on MDL, masking its theoretical underpinnings, functional implications in addition to its numerous interpretations, and its underlying philosophy. the academic contains a short historical past of MDL -- from its roots within the idea of Kolmogorov complexity to the start of MDL right. The publication then provides fresh theoretical advances, introducing glossy MDL tools in a manner that's obtainable to readers from many alternative medical fields. The ebook concludes with examples of the way to use MDL in examine settings that variety from bioinformatics and desktop studying to psychology.

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**Additional resources for Advances in Minimum Description Length: Theory and Applications (Neural Information Processing)**

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First, why did we only reserve code words for θ that are potentially ML estimators for the given data? 4 Information Theory II: Universal Codes and Models 37 by θˆ(k) (D), the ML estimator within θ(k) . Reserving code words for θ ∈ [0, 1]k that cannot be ML estimates would only serve to lengthen L(D | k, θ(k) ) and can never shorten L(k, θ(k) ). Thus, the total description length needed to encode D will increase. Since our stated goal is to minimize description lengths, this is undesirable. However, by the same logic we may also ask whether we have not reserved too many code words for θ ∈ [0, 1]k .

2) 00011000001010100000 . . 1 Information Theory I: Probabilities and Code Lengths 27 We showed that (a) the ﬁrst sequence — an n-fold repetition of 0001 — could be substantially compressed if we use as our code a general-purpose programming language (assuming that valid programs must end with a halt-statement or a closing bracket, such codes satisfy the preﬁx property). We also claimed that (b) the second sequence, n independent outcomes of fair coin tosses, cannot be compressed, and that (c) the third sequence could be compressed to αn bits, with 0 < α < 1.

An easy calculation gives 2−2 log x−1 = P (x) = x∈1,2,... x∈1,2,... 1 1 1 1 = 1, x−2 < + 2 x∈1,2,... 2 2 x=2,3,... x(x − 1) so that P is a (defective) probability distribution. 2), there exists a preﬁx code with, for all k, L(k) = − log P (k) = 2 log k+1. We call the resulting code the ‘simple standard code for the integers’. 4 we will see that it is an instance of a so-called universal code. The idea can be reﬁned to lead to codes with lengths log k+O(log log k); the ‘best’ possible reﬁnement, with code lengths L(k) increasing monotonically but as slowly as possible in k, is known as ‘the universal code for the integers’ [Rissanen 1983].

### Advances in Minimum Description Length: Theory and Applications (Neural Information Processing) by Peter D. Grunwald, In Jae Myung, Mark A. Pitt

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