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Coding Ockham's razor
正題名/作者 ： Coding Ockham's razor/ by Lloyd Allison.
作者 ： Allison, Lloyd.
出版者 ： Cham :Springer International Publishing :2018.
面頁冊數 ： xiv, 175 p. :ill., digital ;24 cm.
Contained By ： Springer eBooks
標題 ： Minimum message length (Information theory) -
電子資源 ： 線上閱讀(Springer)
ISBN ： 9783319764337$q(electronic bk.)
ISBN ： 9783319764320$q(paper)
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245 10$aCoding Ockham's razor$h[electronic resource] /$cby Lloyd Allison.
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300 $axiv, 175 p. :$bill., digital ;$c24 cm.
505 0 $a1 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.
520 $aThis book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory. Accompanied by a library of software, the book can assist an applications programmer, student or researcher in the fields of data analysis and machine learning to write computer programs based upon this principle. MML inference has been around for 50 years and yet only one highly technical book has been written about the subject. The majority of research in the field has been backed by specialised one-off programs but this book includes a library of general MML-based software, in Java. The Java source code is available under the GNU GPL open-source license. The software library is documented using Javadoc which produces extensive cross referenced HTML manual pages. Every probability distribution and statistical model that is described in the book is implemented and documented in the software library. The library may contain a component that directly solves a reader's inference problem, or contain components that can be put together to solve the problem, or provide a standard interface under which a new component can be written to solve the problem. This book will be of interest to application developers in the fields of machine learning and statistics as well as academics, postdocs, programmers and data scientists. It could also be used by third year or fourth year undergraduate or postgraduate students.
650 0$aMinimum message length (Information theory)$3518065
650 14$aComputer Science.$3238508
650 24$aData Structures.$3407955
650 24$aStatistics and Computing/Statistics Programs.$3402169
650 24$aArtificial Intelligence (incl. Robotics)$3401781
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950 $aComputer Science (Springer-11645)
Allison, Lloyd.
Coding Ockham's razor[electronic resource] /by Lloyd Allison. - Cham :Springer International Publishing :2018. - xiv, 175 p. :ill., digital ;24 cm.
1 Introduction -- 2 Discrete -- 3 Integers -- 4 Continuous -- 5 Function-Models -- 6 Multivariate -- 7 Mixture Models -- 8 Function-Models 2 -- 9 Vectors -- 10 Linear Regression -- 11 Graphs -- 12 Bits and Pieces -- 13 An Implementation -- 14 Glossary.
This book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory. Accompanied by a library of software, the book can assist an applications programmer, student or researcher in the fields of data analysis and machine learning to write computer programs based upon this principle. MML inference has been around for 50 years and yet only one highly technical book has been written about the subject. The majority of research in the field has been backed by specialised one-off programs but this book includes a library of general MML-based software, in Java. The Java source code is available under the GNU GPL open-source license. The software library is documented using Javadoc which produces extensive cross referenced HTML manual pages. Every probability distribution and statistical model that is described in the book is implemented and documented in the software library. The library may contain a component that directly solves a reader's inference problem, or contain components that can be put together to solve the problem, or provide a standard interface under which a new component can be written to solve the problem. This book will be of interest to application developers in the fields of machine learning and statistics as well as academics, postdocs, programmers and data scientists. It could also be used by third year or fourth year undergraduate or postgraduate students.
ISBN: 9783319764337$q(electronic bk.)Subjects--Topical Terms:
518065
Minimum message length (Information theory)
LC Class. No.: QA276.9
Dewey Class. No.: 003.54
Coding Ockham's razor[electronic resource] /by Lloyd Allison. - Cham :Springer International Publishing :2018. - xiv, 175 p. :ill., digital ;24 cm.
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.
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.
ISBN: 9783319764337$q(electronic bk.)Subjects--Topical Terms:
518065
Minimum message length (Information theory)
LC Class. No.: QA276.9
Dewey Class. No.: 003.54
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