Statistical and Inductive Inference by Minimum Message Length

C S Wallace

ISBN 10: 038723795X / ISBN 13: 9780387237954

Edité par Springer New York Mai 2005, 2005

A propos de cet article

Neuware - 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.

N° de réf. du vendeur 9780387237954

Détails bibliographiques

• Titre: Statistical and Inductive Inference by Minimum Message Length
• Éditeur: Springer New York Mai 2005
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 038723795X
• ISBN à 13 chiffres: 9780387237954
• Reliure: Buch
• État de l'article: Neu
• Dimensions: 243 L x 156 H x 25 P (en millimètres) 243 millimètres de largeur par 156 millimètres de hauteur par 25 millimètres de profondeur
• Poids: 748 grammes

A propos de ce titre

Synopsis

Since 1965, Prof. Wallace and others have been developing an approach tostatistical estimation, hypothesis testing, model selection and their applications in the Artificial Intelligence field of Machine Learning. The approach is based on Information Theory, using concepts from classical Shannon theory and more recent work on Algorithmic Complexity. The new approach has come to be called the Minimum Message Length principle, since it is based on the idea of constructing a message which concisely encodes the available data. Although a range of journal and conference papers has been published on the principle and its application, and several computer programs applying it have been shown to perform well and have been fairly widely used, there is no text providing a thorough treatment of the principle or giving general guidance for its application.

À propos de l?auteur

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 focused primarily on the Minimum Message Length Principle.