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The Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation of Occam's Razor, asserts that the ?best? explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data.
This book gives a sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practical application. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophy of Science.
Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining.
"Any statistician interested in the foundations of the discipline, or the deeper philosophical issues of inference, will find this volume a rewarding read." Short Book Reviews of the International Statistical Institute, December 2005
Sinossi: Mythanksareduetothemanypeoplewhohaveassistedintheworkreported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine. MyworkleadingtothisbookbeganwhenDavidBoultonandIattempted to develop a method for intrinsic classi?cation. Given data on a sample from some population, we aimed to discover whether the population should be considered to be a mixture of di?erent types, classes or species of thing, and, if so, how many classes were present, what each class looked like, and which things in the sample belonged to which class. I saw the problem as one of Bayesian inference, but with prior probability densities replaced by discrete probabilities re?ecting the precision to which the data would allow parameters to be estimated. Boulton, however, proposed that a classi?cation of the sample was a way of brie?y encoding the data: once each class was described and each thing assigned to a class, the data for a thing would be partially implied by the characteristics of its class, and hence require little further description. After some weeks? arguing our cases, we decided on the maths for each approach, and soon discovered they gave essentially the same results. Without Boulton?s insight, we may never have made the connection between inference and brief encoding, which is the heart of this work.
Titolo: Statistical and Inductive Inference by ...
Casa editrice: Springer
Data di pubblicazione: 2005
Condizione libro: very good
Descrizione libro Springer, 2005. Condizione libro: Very Good. 2005th Edition. N/A. Former Library book. Great condition for a used book! Minimal wear. Codice libro della libreria GRP94897751
Descrizione libro Springer, 2005. Hardcover. Condizione libro: Used: Very Good. This item is printed on demand. Codice libro della libreria SONG038723795X
Descrizione libro Springer 2005-05-26, 2005. Hardcover. Condizione libro: Very Good. 2005. 038723795X Unmarked text. Codice libro della libreria AUGUST-22-THRIFT-06389-JM
Descrizione libro Springer, 2005. Hard Cover. Condizione libro: Good. No Jacket. Ex-library with the usual features. The interior is clean and tight. Binding is good. Cover shows light wear. 429 pages. Ex-Library. Codice libro della libreria 121068
Descrizione libro Condizione libro: Very Good. Book Condition: Very Good. Codice libro della libreria 97803872379543.0
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Descrizione libro Springer 2005-05-26, 2005. Hardcover. Condizione libro: good. 1. 038723795X. Codice libro della libreria 656667
Descrizione libro Springer, 2005. Hardcover. Condizione libro: Used: Good. Codice libro della libreria 12835996
Descrizione libro Springer, 2005. Hardcover. Condizione libro: New. 2005. This item is printed on demand. Codice libro della libreria DADAX038723795X
Descrizione libro Springer, 2017. Hardcover. Condizione libro: Very Good. This item is printed on demand. Codice libro della libreria P02038723795X