000			03348nam a22004575i 4500
001			978-1-4614-6040-4
003			DE-He213
005			20140220082823.0
007			cr nn 008mamaa
008			121205s2013 xxu| s |||| 0|eng d
020			_a9781461460404 _9978-1-4614-6040-4
024	7		_a10.1007/978-1-4614-6040-4 _2doi
050		4	_aQA276-280
072		7	_aPBT _2bicssc
072		7	_aMAT029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aGrover, Jeff. _eauthor.
245	1	0	_aStrategic Economic Decision-Making _h[electronic resource] : _bUsing Bayesian Belief Networks to Solve Complex Problems / _cby Jeff Grover.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_aXI, 116 p. 35 illus., 22 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Statistics, _x2191-544X ; _v9
505	0		_aStrategic Economic Decision Making: The Use of Bayesian Belief Networks (BBN) in Solving Complex Problems -- A Literature Review of Bayes’ Theorem and Bayesian Belief Networks (BBN) -- Statistical Properties of Bayes’ Theorem -- Bayes Belief Networks (BBN) Experimental Protocol -- Manufacturing Example -- Political Science Example -- Gambling Example -- Publicly Traded Company Default Example -- Insurance Risk Levels Example -- Acts of Terrorism Example -- Currency Wars Example -- College Entrance Exams Example -- Special Forces Assessment and Selection (SFAS) One-Stage Example -- Special Forces Assessment and Selection (SFAS) Two-Stage Example.
520			_aStrategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes’ theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes’ theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes’ model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study.  Very little has been published in the area of discrete Bayes’ theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.
650		0	_aStatistics.
650		0	_aMathematical statistics.
650	1	4	_aStatistics.
650	2	4	_aStatistics, general.
650	2	4	_aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
650	2	4	_aStatistical Theory and Methods.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461460398
830		0	_aSpringerBriefs in Statistics, _x2191-544X ; _v9
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-6040-4
912			_aZDB-2-SMA
999			_c95571 _d95571