Decisions under Uncertainty: Probabilistic Analysis for Engineering DecisionsCambridge University Press, 7 d’abr. 2005 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 2005 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
| 1 | |
| 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 |
Risk safety and reliability | 514 |
Data and simulation | 561 |
Conclusion | 618 |
Common probability distributions | 620 |
Mathematical aspects | 637 |
Answers and comments on exercises | 643 |
| 650 | |
| 657 | |
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 balls Bayes Bayesian binomial distribution calculation Chapter chi-square chi-square distribution coefficient conjugate prior consider constant constraints corresponding decision-making defined denoted density derived discrete engineering entropy equal Equation estimate example Exercise expected value exponential distribution extremal distribution failure Finetti frequency function fX(x gamma gamma distribution given Gumbel Gumbel distribution illustrated independent inference integral interval judgement linear load lottery matrix maximum Mean and variance measure method normal distribution notation noted obtain one’s optimal outcomes parameter payoff payoff matrix Poisson Poisson distribution possible Pr(X prior distribution probabilistic probability distribution probability mass problem random quantities represents result risk aversion sample scale Section shows solution standard deviation strategies structure Table theorem theory transformation trials uncertainty utility variables zero