000			03960nam a22004815i 4500
001			978-0-387-27656-4
003			DE-He213
005			20250710083939.0
007			cr nn 008mamaa
008			100301s2005 xxu| s |||| 0|eng d
020			_a9780387276564 _a99780387276564
024	7		_a10.1007/0-387-27656-4 _2doi
082	0	4	_a519.5 _223
100	1		_aWallace, C.S. _eauthor.
245	1	0	_aStatistical and Inductive Inference by Minimum Message Length _h[recurso electrónico] / _cby C.S. Wallace.
264		1	_aNew York, NY : _bSpringer New York, _c2005.
300			_aXVI, 432 p. 22 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aInformation Science and Statistics, _x1613-9011
505	0		_aInductive Inference -- Information -- Strict Minimum Message Length (SMML) -- Approximations to SMML -- MML: Quadratic Approximations to SMML -- MML Details in Some Interesting Cases -- Structural Models -- The Feathers on the Arrow of Time -- MML as a Descriptive Theory -- Related Work.
520			_aThe Minimum Message Length (MML) Principle is an information-theoretic approach to induction, hypothesis testing, model selection, and statistical inference. MML, which provides a formal specification for the implementation of Occam's Razor, asserts that the 'best' explanation of observed data is the shortest. Further, an explanation is acceptable (i.e. the induction is justified) only if the explanation is shorter than the original data. This book gives a sound introduction to the Minimum Message Length Principle and its applications, provides the theoretical arguments for the adoption of the principle, and shows the development of certain approximations that assist its practical application. MML appears also to provide both a normative and a descriptive basis for inductive reasoning generally, and scientific induction in particular. The book describes this basis and aims to show its relevance to the Philosophy of Science. Statistical and Inductive Inference by Minimum Message Length will be of special interest to graduate students and researchers in Machine Learning and Data Mining, scientists and analysts in various disciplines wishing to make use of computer techniques for hypothesis discovery, statisticians and econometricians interested in the underlying theory of their discipline, and persons interested in the Philosophy of Science. The book could also be used in a graduate-level course in Machine Learning and Estimation and Model-selection, Econometrics and Data Mining. C.S. Wallace was appointed Foundation Chair of Computer Science at Monash University in 1968, at the age of 35, where he worked until his death in 2004. He received an ACM Fellowship in 1995, and was appointed Professor Emeritus in 1996. Professor Wallace made numerous significant contributions to diverse areas of Computer Science, such as Computer Architecture, Simulation and Machine Learning. His final research focused primarily on the Minimum Message Length Principle.
650		0	_aSTATISTICS.
650		0	_aCODING THEORY.
650		0	_aCOMPUTER SCIENCE.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aMATHEMATICAL STATISTICS.
650	1	4	_aSTATISTICS.
650	2	4	_aSTATISTICAL THEORY AND METHODS.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aCODING AND INFORMATION THEORY.
650	2	4	_aPROBABILITY AND STATISTICS IN COMPUTER SCIENCE.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387237954
830		0	_aInformation Science and Statistics, _x1613-9011
856	4	0	_uhttp://dx.doi.org/10.1007/0-387-27656-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SMA
942			_2ddc _cER
999			_c56726 _d56726