000			02044nam a22003017a 4500
001			sulb-eb0016750
003			BD-SySUS
005			20160405140620.0
008			100519s2010||||enk o ||1 0|eng|d
020			_a9780511779237 (ebook)
020			_z9780521764544 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP _dBD-SySUS.
050	0	0	_aQA279.5 _b.S628 2010
082	0	0	_a519.5/42 _222
100	1		_aSmith, Jim Q., _eauthor.
245	1	0	_aBayesian Decision Analysis : _bPrinciples and Practice / _cJim Q. Smith.
264		1	_aCambridge : _bCambridge University Press, _c2010.
300			_a1 online resource (348 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 04 Apr 2016).
520			_aBayesian decision analysis supports principled decision making in complex domains. This textbook takes the reader from a formal analysis of simple decision problems to a careful analysis of the sometimes very complex and data rich structures confronted by practitioners. The book contains basic material on subjective probability theory and multi-attribute utility theory, event and decision trees, Bayesian networks, influence diagrams and causal Bayesian networks. The author demonstrates when and how the theory can be successfully applied to a given decision problem, how data can be sampled and expert judgements elicited to support this analysis, and when and how an effective Bayesian decision analysis can be implemented. Evolving from a third-year undergraduate course taught by the author over many years, all of the material in this book will be accessible to a student who has completed introductory courses in probability and mathematical statistics.
650		0	_aBayesian statistical decision theory
776	0	8	_iPrint version: _z9780521764544
856	4	0	_uhttp://dx.doi.org/10.1017/CBO9780511779237
942			_2Dewey Decimal Classification _ceBooks
999			_c38188 _d38188