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 a ~besta (TM) 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.
"Any statistician interested in the foundations of the
discipline, or the deeper philosophical issues of inference, will
find this volume a rewarding read." Short Book Reviews of
theInternational Statistical Institute, December 2005
General
| Imprint: |
Springer-Verlag New York
|
| Country of origin: |
United States |
| Series: |
Information Science and Statistics |
| Release date: |
May 2005 |
| First published: |
2005 |
| Authors: |
C.S. Wallace
|
| Dimensions: |
235 x 155 x 25mm (L x W x T) |
| Format: |
Hardcover
|
| Pages: |
432 |
| Edition: |
2005 ed. |
| ISBN-13: |
978-0-387-23795-4 |
| Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
Promotions
|
| LSN: |
0-387-23795-X |
| Barcode: |
9780387237954 |
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