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Название: Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Автор: Wallace C.
My thanks are due to the many people who have assisted in the work reported
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.
My work leading to this book began when David Boulton and I attempted
to develop a method for intrinsic classification. Given data on a sample from
some population, we aimed to discover whether the population should be
considered to be a mixture of different 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 reflecting the precision to which the data would allow
parameters to be estimated. Boulton, however, proposed that a classification
of the sample was a way of briefly 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.