Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/1012
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dc.contributor.authorAllison, Lloyd. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:24:24Z-
dc.date.available2020-05-17T08:24:24Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319764337 ;en_US
dc.identifier.isbn9783319764320 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1012-
dc.descriptionen_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319764320. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis 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 MMLeebased 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. ;en_US
dc.description.statementofresponsibilityby Lloyd Allison.en_US
dc.description.tableofcontents1 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. ;en_US
dc.format.extentXIV, 175 p. 46 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.haspart9783319764337.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectData structures (Computer science). ;en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectStatistics. ;en_US
dc.subjectComputer Scienceen_US
dc.subjectData Structures. ;en_US
dc.subjectStatistics and Computing/Statistics Programs. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subject.ddc005.73 ; 23 ;en_US
dc.subject.lccQA76.9.D35 ;en_US
dc.titleCoding Ockham's Razoren_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
Appears in Collections:مدیریت فناوری اطلاعات

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dc.contributor.authorAllison, Lloyd. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:24:24Z-
dc.date.available2020-05-17T08:24:24Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319764337 ;en_US
dc.identifier.isbn9783319764320 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1012-
dc.descriptionen_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319764320. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis 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 MMLeebased 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. ;en_US
dc.description.statementofresponsibilityby Lloyd Allison.en_US
dc.description.tableofcontents1 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. ;en_US
dc.format.extentXIV, 175 p. 46 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.haspart9783319764337.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectData structures (Computer science). ;en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectStatistics. ;en_US
dc.subjectComputer Scienceen_US
dc.subjectData Structures. ;en_US
dc.subjectStatistics and Computing/Statistics Programs. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subject.ddc005.73 ; 23 ;en_US
dc.subject.lccQA76.9.D35 ;en_US
dc.titleCoding Ockham's Razoren_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319764337.pdf2.89 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAllison, Lloyd. ;en_US
dc.date.accessioned2013en_US
dc.date.accessioned2020-05-17T08:24:24Z-
dc.date.available2020-05-17T08:24:24Z-
dc.date.issued2018en_US
dc.identifier.isbn9783319764337 ;en_US
dc.identifier.isbn9783319764320 (print) ;en_US
dc.identifier.urihttp://localhost/handle/Hannan/1012-
dc.descriptionen_US
dc.descriptionSpringerLink (Online service) ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionPrinted edition: ; 9783319764320. ;en_US
dc.descriptionen_US
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractThis 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 MMLeebased 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. ;en_US
dc.description.statementofresponsibilityby Lloyd Allison.en_US
dc.description.tableofcontents1 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. ;en_US
dc.format.extentXIV, 175 p. 46 illus. ; online resource. ;en_US
dc.publisherSpringer International Publishing :en_US
dc.publisherImprint: Springer,en_US
dc.relation.haspart9783319764337.pdfen_US
dc.subjectComputer Scienceen_US
dc.subjectData structures (Computer science). ;en_US
dc.subjectArtificial Intelligenceen_US
dc.subjectStatistics. ;en_US
dc.subjectComputer Scienceen_US
dc.subjectData Structures. ;en_US
dc.subjectStatistics and Computing/Statistics Programs. ;en_US
dc.subjectArtificial Intelligence and Roboticsen_US
dc.subject.ddc005.73 ; 23 ;en_US
dc.subject.lccQA76.9.D35 ;en_US
dc.titleCoding Ockham's Razoren_US
dc.typeBooken_US
dc.publisher.placeCham :en_US
Appears in Collections:مدیریت فناوری اطلاعات

Files in This Item:
File Description SizeFormat 
9783319764337.pdf2.89 MBAdobe PDFThumbnail
Preview File