Statistical and Inductive Inference by Minimum Message Length

C S Wallace

ISBN 10: 038723795X / ISBN 13: 9780387237954

Editore: Springer New York Mai 2005, 2005

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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.

Codice articolo 9780387237954

Dati bibliografici

• Titolo: Statistical and Inductive Inference by Minimum Message Length
• Editore: Springer New York Mai 2005
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 038723795X
• ISBN 13: 9780387237954
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 243 L x 156 A x 25 P (millimetri) 243 millimetri di larghezza per 156 millimetri di altezza per 25 millimetri di profondità
• Peso: 748 grammi

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

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.