000			03563nam a2200493 i 4500
001			6267274
003			IEEE
005			20220712204616.0
006			m o d
007			cr |n|||||||||
008			151223s2007 maua ob 001 eng d
020			_a9780262256292 _qebook
020			_z0262072815 _qalk. paper
020			_z9780262072816 _qhardback
020			_z0262256290 _qelectronic
035			_a(CaBNVSL)mat06267274
035			_a(IDAMS)0b000064818b4260
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQA276.9 _b.G78 2007eb
100	1		_aGr�unwald, Peter D., _eauthor. _921876
245	1	4	_aThe minimum description length principle / _cPeter D. Gr�unwald.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc2007.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2007]
300			_a1 PDF (xxxii, 703 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aAdaptive computation and machine learning series
504			_aIncludes bibliographical references (p. [651]-673) and indexes.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aThe minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data. MDL methods are particularly well-suited for dealing with model selection, prediction, and estimation problems in situations where the models under consideration can be arbitrarily complex, and overfitting the data is a serious concern.This extensive, step-by-step introduction to the MDL Principle provides a comprehensive reference (with an emphasis on conceptual issues) that is accessible to graduate students and researchers in statistics, pattern classification, machine learning, and data mining, to philosophers interested in the foundations of statistics, and to researchers in other applied sciences that involve model selection, including biology, econometrics, and experimental psychology. Part I provides a basic introduction to MDL and an overview of the concepts in statistics and information theory needed to understand MDL. Part II treats universal coding, the information-theoretic notion on which MDL is built, and part III gives a formal treatment of MDL theory as a theory of inductive inference based on universal coding. Part IV provides a comprehensive overview of the statistical theory of exponential families with an emphasis on their information-theoretic properties. The text includes a number of summaries, paragraphs offering the reader a "fast track" through the material, and boxes highlighting the most important concepts.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
550			_aMade available online by EBSCO.
588			_aDescription based on PDF viewed 12/23/2015.
650		0	_aMinimum description length (Information theory) _921877
655		0	_aElectronic books. _93294
710	2		_aIEEE Xplore (Online Service), _edistributor. _921878
710	2		_aMIT Press, _epublisher. _921879
776	0	8	_iPrint version _z9780262072816
830		0	_aAdaptive computation and machine learning. _921570
856	4	2	_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267274
942			_cEBK
999			_c72932 _d72932