Coding Ockham's Razor / by Lloyd Allison.

Format:

Book

Author/Creator:

Allison, Lloyd, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Language:

English

Subjects (All):

Data structures (Computer science).

Statistics.

Artificial intelligence.

Data Structures.

Statistics and Computing/Statistics Programs.

Artificial Intelligence.

Local Subjects:

Data Structures.

Statistics and Computing/Statistics Programs.

Artificial Intelligence.

Physical Description:

1 online resource (XIV, 175 pages) : 46 illustrations

Edition:

First edition 2018.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

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 documented in 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.

Contents:

1 Introduction

2 Discrete

3 Integers

4 Continuous

5 Function-Models

6 Multivariate

7 Mixture Models

8 Function-Models 2

9 Vectors

10 Linear Regression

11 Graphs

12 Bits and Pieces

13 An Implementation

14 Glossary.

Other Format:

Printed edition:

ISBN:

978-3-319-76433-7

9783319764337

9783319764320

9783319764344

9783030094881

Access Restriction:

Restricted for use by site license.