Decisions Under Uncertainty: Probabilistic Analysis for Engineering Decisions
Cambridge 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.
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Probability distributions expectation and prevision
The concept of utility
Characteristic functions transformed and limiting distributions
Exchangeability and inference
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Decisions under Uncertainty: Probabilistic Analysis for Engineering Decisions
Previsualització limitada - 2005
analysis applied assignment assume attributes balls becomes binomial calculation Chapter common consequences consider constant constraints continuous corresponding decision defined denoted density derived discrete discussed engineering entropy equal Equation estimate example Exercise expected experiment expression extreme failure Figure function further given gives illustrated important increasing independent indicate integral interest interval introduced linear mass maximum mean measure method nature normal distribution noted objective obtain occur optimal parameter particular positive possible present prior probability probability distribution problem question random quantities reasonable reference regarding relationship represents respectively result risk sample scale shown shows situation solution space standard deviation strategies structure success Table theory transformation trials uncertainty units utility variables variance wish zero