Quantitative Risk Analysis: Guide to Monte Carlo Simulation Modelling - Tapa dura

Risk Analysis A Quantitative Guide Risk and uncertainty are key features of most business and government problems and need to be understood for rational decisions to be made. This book concerns itself with the quantification of risk, the modelling of identified risks and how to make decisions from those models. Following on from the success of the previous edition of this clearly written and highly regarded book, this edition is extensively revised and updated and will provide an invaluable practical guide for beginners and experienced practitioners alike. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modelling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems. Reviews of the first edition "It identifies the various facets of risk analysis and provides a valuable reference to the concepts and techniques employed." Project, 1997 "It clearly explains many essential aspects of quantitative risk analysis . provides valuable techniques and sound professional advice." Journal of Behavioral Decision Making, Vol. 12, 1999 "The book offers a powerful method for dealing with risk and uncertainty." Zentralblatt fur Mathematik, Band 908, 1999

The Ten Commandments to quantitative risk analysis Morgan and Henrion, 1990
* Do your homework with literature, experts and users
* Let the problem drive the analysis
* Make the analysis as simple as possible, but no simpler
* Identify all significant assumptions
* Be explicit about decision criteria and policy strategy
* Be explicit about uncertainties
* Perform systematic sensitivity and uncertainty analysis
* Iteratively refine the problem statement and analysis
* Document clearly and completely
* Expose to peer review