Decisions Under Uncertainty: Probabilistic Analysis for Engineering DecisionsCambridge University Press, 7 d’abr. 2005 - 672 pàgines To better understand the core concepts of probability and to see how they affect real-world decisions about design and system performance, engineers and scientists might want to ask themselves the following questions: What exactly is meant by probability? What is the precise definition of the 100-year load and how is it calculated? What is an "extremal" probability distribution? What is the Bayesian approach? How is utility defined? How do games fit into probability theory? What is entropy? How do I apply these ideas in risk analysis? Starting from the most basic assumptions, this book develops a coherent theory of probability and broadens it into applications in decision theory, design, and risk analysis. This book is written for engineers and scientists interested in probability and risk. It can be used by undergraduates, graduate students, or practicing engineers. |
Continguts
2 | 20 |
Probability distributions expectation and prevision | 90 |
The concept of utility | 162 |
Games and optimization | 220 |
Entropy | 272 |
Characteristic functions transformed and limiting distributions | 317 |
Exchangeability and inference | 378 |
Extremes | 452 |
Altres edicions - Mostra-ho tot
Decisions under Uncertainty: Probabilistic Analysis for Engineering Decisions Ian Jordaan Previsualització limitada - 2005 |
Decisions under Uncertainty: Probabilistic Analysis for Engineering Decisions Ian Jordaan Previsualització no disponible - 2011 |
Frases i termes més freqüents
analysis applied assignment assume attributes balls becomes binomial calculation Chapter common consequences consider constant constraints continuous corresponding decision defined denoted density derived discrete discussed drawing engineering entropy equal Equation estimate example Exercise expected experiment expression extreme failure Figure function further given gives illustrated important increasing independent indicate integral interest interpretation interval introduced linear mass mean measure method nature normal distribution noted objective obtain optimal parameter particular positive possible present prior probability problem question random quantities reasonable reference regarding relationship represents respectively result risk sample scale shown shows situation solution space standard deviation strategies structure successes Table theory transformation trials uncertainty units utility variables variance weight wish zero
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Pàgina iii - Faculty of Engineering and Applied Science Memorial University of Newfoundland St. John's, Newfoundland...