Intl. Ed.
Intl. Ed.
STATISTICAL AND INDUCTIVE INFERENCE BY MINIMUM MESSAGE LENGTH (HB) Hardback - 2005
by WALLACE C.S
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Details
- Title STATISTICAL AND INDUCTIVE INFERENCE BY MINIMUM MESSAGE LENGTH (HB)
- Author WALLACE C.S
- Binding Hardback
- Edition USA Edition
- Condition New
- Pages 432
- Volumes 1
- Language ENG
- Publisher Springer, Secaucus, New Jersey, U.S.A.
- Publication date 2005-05-26
- Illustrated Yes
- Features Bibliography, Illustrated, Index
- Bookseller's Inventory # cbs 9780387237954
- ISBN 9780387237954 / 038723795X
- Weight 1.65 lbs (0.75 kg)
- Dimensions 9.56 x 6.16 x 0.99 in (24.28 x 15.65 x 2.51 cm)
- Category Mathematics
- Library of Congress subjects Induction (Mathematics), Minimum message length (Information theory)
- Library of Congress Catalogue Number 2004059195
- Dewey Decimal Code 519.5
- Quantity available 8
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From the publisher
First line
The best explanation of the facts is the shortest.
From the rear cover
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.
C.S. Wallace was appointed Foundation Chair of Computer Science at Monash University in 1968, at the age of 35, where he worked until his death in 2004. He received an ACM Fellowship in 1995, and was appointed Professor Emeritus in 1996. Professor Wallace made numerous significant contributions to diverse areas of Computer Science, such as Computer Architecture, Simulation and Machine Learning. His final research focusedprimarily on the Minimum Message Length Principle.
