What Is Probabilistic Analysis
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Probability and Computing
Author: Michael Mitzenmacher
language: en
Publisher: Cambridge University Press
Release Date: 2005-01-31
Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This 2005 textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, the probabilistic method and Markov chains. The second half covers more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool.
Probabilistic Risk Analysis
Author: Tim Bedford
language: en
Publisher: Cambridge University Press
Release Date: 2001-04-30
Probabilistic risk analysis aims to quantify the risk caused by high technology installations. Increasingly, such analyses are being applied to a wider class of systems in which problems such as lack of data, complexity of the systems, uncertainty about consequences, make a classical statistical analysis difficult or impossible. The authors discuss the fundamental notion of uncertainty, its relationship with probability, and the limits to the quantification of uncertainty. Drawing on extensive experience in the theory and applications of risk analysis, the authors focus on the conceptual and mathematical foundations underlying the quantification, interpretation and management of risk. They cover standard topics as well as important new subjects such as the use of expert judgement and uncertainty propagation. The relationship of risk analysis with decision making is highlighted in chapters on influence diagrams and decision theory. Finally, the difficulties of choosing metrics to quantify risk, and current regulatory frameworks are discussed.
Probabilistic Methods in Geotechnical Engineering
Author: D. V. Griffiths
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-12-14
Learn to use probabilistic techniques to solve problems in geotechnical engineering. The book reviews the statistical theories needed to develop the methodologies and interpret the results. Next, the authors explore probabilistic methods of analysis, such as the first order second moment method, the point estimate method, and random set theory. Examples and case histories guide you step by step in applying the techniques to particular problems.