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Coding Ockham's Razor

Coding Ockham's Razor

Coding Ockham's Razor
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Coding Ockham's Razor Hardback - 2018

by Allison, Lloyd

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Springer, 2018-05-18. 1st ed. 2018. hardcover. Used: Good. 6.14x0.50x9.21. Buy with confidence. Excellent Customer Service & Return policy.
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Details

  • Title Coding Ockham's Razor
  • Author Allison, Lloyd
  • Binding Hardback
  • Edition 1st ed. 2018
  • Condition Used: Good
  • Pages 175
  • Volumes 1
  • Language ENG
  • Publisher Springer
  • Publication date 2018-05-18
  • Illustrated Yes
  • Features Glossary, Illustrated
  • Bookseller's Inventory # SONG3319764322
  • ISBN 9783319764320 / 3319764322
  • Weight 0.99 lbs (0.45 kg)
  • Dimensions 9.21 x 6.14 x 0.5 in (23.39 x 15.60 x 1.27 cm)
  • Size 6.14x0.50x9.21
  • Category Computers - Data Base Management
  • Dewey Decimal Code 005.73
  • Quantity available 1

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Reader reviews for Coding Ockham's Razor

From the publisher

This book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory. Accompanied by a library of software, the book can assist an applications programmer, student or researcher in the fields of data analysis and machine learning to write computer programs based upon this principle.

MML inference has been around for 50 years and yet only one highly technical book has been written about the subject. The majority of research in the field has been backed by specialised one-off programs but this book includes a library of general MML-based software, in Java. The Java source code is available under the GNU GPL open-source license. The software library is documented using Javadoc which produces extensive cross referenced HTML manual pages. Every probability distribution and statistical model that is described in the book is implemented and documentedin the software library. The library may contain a component that directly solves a reader's inference problem, or contain components that can be put together to solve the problem, or provide a standard interface under which a new component can be written to solve the problem.

This book will be of interest to application developers in the fields of machine learning and statistics as well as academics, postdocs, programmers and data scientists. It could also be used by third year or fourth year undergraduate or postgraduate students.

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