東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁 到查詢結果 [ subject:"Minimum message length (Information theory)" ]

切換: 標籤 | MARC模式 | ISBD

Coding Ockham's razor

Allison, Lloyd.

FindBook

Google Book

Amazon

博客來

Coding Ockham's razor

紀錄類型:	書目-電子資源 : Monograph/item
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.
內容註:	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.
Contained By:	Springer eBooks
標題:	Minimum message length (Information theory) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-76433-7
ISBN:	9783319764337

Coding Ockham's razor
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

Standard No.: 10.1007/978-3-319-76433-7doiSubjects--Topical Terms:

897314
Minimum message length (Information theory)

LC Class. No.: QA276.9

Dewey Class. No.: 003.54

Coding Ockham's razor
LDR:02645nmm a2200313 a 4500 001 2145868
003 DE-He213
005 20180504135139.0
006 m d
007 cr nn 008maaau
008 190227s2018 gw s 0 eng d
020 $a 9783319764337 $q (electronic bk.)
020 $a 9783319764320 $q (paper)
024 7 $a 10.1007/978-3-319-76433-7 $2 doi
035 $a 978-3-319-76433-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.9
072 7 $a UMB $2 bicssc
072 7 $a COM062000 $2 bisacsh
082 0 4 $a 003.54 $2 23
090 $a QA276.9 $b .A438 2018
100 1 $a Allison, Lloyd. $3 3331865
245 1 0 $a Coding Ockham's razor $h [electronic resource] / $c by Lloyd Allison.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a xiv, 175 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Minimum message length (Information theory) $3 897314
650 1 4 $a Computer Science. $3 626642
650 2 4 $a Data Structures. $3 891009
650 2 4 $a Statistics and Computing/Statistics Programs. $3 894293
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 890894
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-76433-7
950 $a Computer Science (Springer-11645)