000			03329nam a2200517 i 4500
001			6267502
003			IEEE
005			20190220121648.0
006			m o d
007			cr |n|||||||||
008			151229s1996 maua ob 001 eng d
010			_z 96012572 (print)
020			_a9780262290999 _qelectronic
020			_z9780262193689 _qprint
020			_z026219368X _qalk. paper
035			_a(CaBNVSL)mat06267502
035			_a(IDAMS)0b000064818b4510
040			_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL
050		4	_aQ335 _b.S45 1996eb
082	0	0	_a519.2/01 _220
100	1		_aShafer, Glenn, _d1946-
245	1	4	_aThe art of causal conjecture / _cGlenn Shafer.
264		1	_aCambridge, Massachusetts : _bMIT Press, _cc1996.
264		2	_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[1996]
300			_a1 PDF (xx, 511 pages) : _billustrations.
336			_atext _2rdacontent
337			_aelectronic _2isbdmedia
338			_aonline resource _2rdacarrier
490	1		_aArtificial intelligence series
504			_aIncludes bibliographical references (p. [491]-500) and index.
506	1		_aRestricted to subscribers or individual electronic text purchasers.
520			_aIn The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial intelligence, and philosophy.The various disciplines that use causal reasoning differ in the relative weight they put on security and precision of knowledge as opposed to timeliness of action. The natural and social sciences seek high levels of certainty in the identification of causes and high levels of precision in the measurement of their effects. The practical sciences--medicine, business, engineering, and artificial intelligence--must act on causal conjectures based on more limited knowledge. Shafer's understanding of causality contributes to both of these uses of causal reasoning. His language for causal explanation can guide statistical investigation in the natural and social sciences, and it can also be used to formulate assumptions of causal uniformity needed for decision making in the practical sciences.Causal ideas permeate the use of probability and statistics in all branches of industry, commerce, government, and science. The Art of Causal Conjecture shows that causal ideas can be equally important in theory. It does not challenge the maxim that causation cannot be proven from statistics alone, but by bringing causal ideas into the foundations of probability, it allows causal conjectures to be more clearly quantified, debated, and confronted by statistical evidence.
530			_aAlso available in print.
538			_aMode of access: World Wide Web
588			_aDescription based on PDF viewed 12/29/2015.
650		0	_aArtificial intelligence.
650		0	_aCausation.
650		0	_aPrediction (Logic)
650		0	_aProbabilities.
655		0	_aElectronic books.
710	2		_aIEEE Xplore (Online Service), _edistributor.
710	2		_aMIT Press, _epublisher.
776	0	8	_iPrint version: _z9780262193689
830		0	_aArtificial intelligence (Cambridge, Mass.)
856	4	2	_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267502
999			_c39414 _d39414