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010   $a3-319-76433-0
024 7 $a10.1007/978-3-319-76433-7
035   $a(CKB)4100000004243715
035   $a(DE-He213)978-3-319-76433-7
035   $a(MiAaPQ)EBC5387364
035   $a(PPN)227406214
035   $a(EXLCZ)994100000004243715
100   $a20180504d2018      u|         0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aCoding Ockham's Razor /$fby Lloyd Allison
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (XIV, 175 p. 46 illus.) 
311   $a3-319-76432-2 
320   $aIncludes bibliographical references and index.
327   $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.
330   $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.
606   $aData structures (Computer science)
606   $aStatistics
606   $aArtificial intelligence
606   $aData Structures$3https://scigraph.springernature.com/ontologies/product-market-codes/I15017
606   $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
615  0$aData structures (Computer science)
615  0$aStatistics.
615  0$aArtificial intelligence.
615 14$aData Structures.
615 24$aStatistics and Computing/Statistics Programs.
615 24$aArtificial Intelligence.
676   $a005.73
700   $aAllison$b Lloyd$4aut$4http://id.loc.gov/vocabulary/relators/aut$0875498
906   $aBOOK
912   $a9910299270503321
996   $aCoding Ockham's Razor$91954794
997   $aUNINA