| 000 | 02522nam a22003258i 4500 | ||
|---|---|---|---|
| 001 | CR9780511779237 | ||
| 003 | UkCbUP | ||
| 005 | 20200124160252.0 | ||
| 006 | m|||||o||d|||||||| | ||
| 007 | cr|||||||||||| | ||
| 008 | 100519s2010||||enk o ||1 0|eng|d | ||
| 020 | _a9780511779237 (ebook) | ||
| 020 | _z9780521764544 (hardback) | ||
| 040 |
_aUkCbUP _beng _erda _cUkCbUP |
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| 050 | 0 | 0 |
_aQA279.5 _b.S628 2010 |
| 082 | 0 | 0 |
_a519.5/42 _222 |
| 100 | 1 |
_aSmith, J. Q., _d1953- _eauthor. |
|
| 245 | 1 | 0 |
_aBayesian decision analysis : _bprinciples and practice / _cJim Q. Smith. |
| 264 | 1 |
_aCambridge : _bCambridge University Press, _c2010. |
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| 300 |
_a1 online resource (ix, 338 pages) : _bdigital, PDF file(s). |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 500 | _aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). | ||
| 505 | 8 | _aMachine generated contents note: Preface; Part I. Foundations of Decision Modeling: 1. Introduction; 2. Explanations of processes and trees; 3. Utilities and rewards; 4. Subjective probability and its elicitation; 5. Bayesian inference for decision analysis; Part II. Multi-Dimensional Decision Modeling: 6. Multiattribute utility theory; 7. Bayesian networks; 8. Graphs, decisions and causality; 9. Multidimensional learning; 10. Conclusions; Bibliography. | |
| 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 | _uhttps://doi.org/10.1017/CBO9780511779237 |
| 999 |
_c519640 _d519638 |
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