Coding Ockham's Razor Coding Ockham's Razor

Coding Ockham's Razor

    • £67.99
    • £67.99

Publisher Description

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.

GENRE
Computing & Internet
RELEASED
2018
4 May
LANGUAGE
EN
English
LENGTH
189
Pages
PUBLISHER
Springer International Publishing
SIZE
3.1
MB
Modeling Decisions for Artificial Intelligence Modeling Decisions for Artificial Intelligence
2021
Algorithmic Learning in a Random World Algorithmic Learning in a Random World
2022
Combining Soft Computing and Statistical Methods in Data Analysis Combining Soft Computing and Statistical Methods in Data Analysis
2010
Handbook of Computational Statistics Handbook of Computational Statistics
2012
Belief Functions: Theory and Applications Belief Functions: Theory and Applications
2021
Rough Sets Rough Sets
2021