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Books > Reference & Interdisciplinary > Communication studies > Coding theory & cryptology
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Statistical and Inductive Inference by Minimum Message Length (Paperback, Softcover reprint of hardcover 1st ed. 2005)
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Statistical and Inductive Inference by Minimum Message Length (Paperback, Softcover reprint of hardcover 1st ed. 2005)
Series: Information Science and Statistics
Expected to ship within 10 - 15 working days
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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.
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