Probabilistic Risk Analysis Foundations And Methods Pdf
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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.
Risk Analysis Foundations, Models, and Methods
Author: Louis Anthony Cox Jr.
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Risk Analysis: Foundations, Models, and Methods fully addresses the questions of "What is health risk analysis?" and "How can its potentialities be developed to be most valuable to public health decision-makers and other health risk managers?" Risk analysis provides methods and principles for answering these questions. It is divided into methods for assessing, communicating, and managing health risks. Risk assessment quantitatively estimates the health risks to individuals and to groups from hazardous exposures and from the decisions or activities that create them. It applies specialized models and methods to quantify likely exposures and their resulting health risks. Its goal is to produce information to improve decisions. It does this by relating alternative decisions to their probable consequences and by identifying those decisions that make preferred outcomes more likely. Health risk assessment draws on explicit engineering, biomathematical, and statistical consequence models to describe or simulate the causal relations between actions and their probable effects on health. Risk communication characterizes and presents information about health risks and uncertainties to decision-makers and stakeholders. Risk management applies principles for choosing among alternative decision alternatives or actions that affect exposure, health risks, or their consequences.
Modern Statistical and Mathematical Methods in Reliability
This volume contains extended versions of 28 carefully selected and reviewed papers presented at The Fourth International Conference on Mathematical Methods in Reliability in Santa Fe, New Mexico, June 21-25, 2004, the leading conference in reliability research. A broad overview of current research activities in reliability theory and its applications is provided with coverage on reliability modeling, network and system reliability, Bayesian methods, survival analysis, degradation and maintenance modeling, and software reliability. The contributors are all leading experts in the field and include the plenary session speakers, Tim Bedford, Thierry Duchesne, Henry Wynn, Vicki Bier, Edsel Pena, Michael Hamada, and Todd Graves.