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Preface.Part 1: Introduction.1. Why do a risk analysis?1.1. Moving on from “What If” Scenarios.1.2. The Risk Analysis Process.1.3. Risk Management Options.1.4. Evaluating Risk Management Options.1.5. Inefficiencies in Transferring Risks to Others.1.6. Risk Registers.2. Planning a risk analysis.2.1. Questions and Motives.2.2. Determine the Assumptions that are Acceptable or Required.2.3. Time and Timing.2.4. You’ll Need a Good Risk Analyst or Team.3. The quality of a risk analysis.3.1. The Reasons Why a Risk Analysis can be Terrible.3.2. Communicating the Quality of Data Used in a Risk Analysis.3.3. Level of Criticality.3.4. The Biggest Uncertainty in a Risk Analysis.3.5. Iterate.4. Choice of model structure.4.1. Software Tools and the Models they Build.4.2. Calculation Methods.4.3. Uncertainty and Variability.4.4. How Monte Carlo Simulation Works.4.5. Simulation Modelling.5. Understanding and using the results of a risk analysis.5.1. Writing a Risk Analysis Report.5.2. Explaining a Model’s Assumptions.5.3. Graphical Presentation of a Model’s Results.5.4. Statistical Methods of Analysing Results.Part 2: Introduction.6. Probability mathematics and simulation.6.1. Probability Distribution Equations.6.2. The Definition of “Probability”.6.3. Probability Rules.6.4. Statistical Measures.7. Building and running a model.7.1. Model Design and Scope.7.2. Building Models that are Easy to Check and Modify.7.3. Building Models that are Efficient.7.4. Most Common Modelling Errors.8. Some basic random processes.8.1. Introduction.8.2. The Binomial Process.8.3. The Poisson Process.8.4. The Hypergeometric Process.8.5. Central Limit Theorem.8.6. Renewal Processes.8.7. Mixture Distributions.8.8. Martingales.8.9. Miscellaneous Example.&

David Vose is senior partner of Vose Consulting, a risk analysis consulting, software and training firm with offices in the US, Europe and Russia. He has worked in risk analysis since 1988 in an extensive range of industry and government problems from insurance, banking, corporate finance, food safety, nuclear power, and epidemiology to oil and gas, construction, utilities, and general commerce. he has co-authored and edited several international guidelines on risk. A charismatic speaker, David gives frequent public and in-house risk analysis seminars. David has served as expert witness in a variety of high profile court cases. A keen squash player, he lives with Veerle and their two children in Ghent, Belgium and dreams of one day owning an old Bentley when there's room in the garage..

Risk Analysis: A Quantitative Guide is a comprehensive guide for eh risk analyst and decision maker. based on the author's extensive experience in solving real-world risk problems, this book is an invaluable aid to the risk analysis practitioner. by providing the building blocks of risk-based thinking the author guides the reader through the steps necessary to produce a realistic risk-based thinking the author guides the reader through the steps necessary to produce a realistic risk analysis and offers general and specific techniques to cope with most common and challenging risk modelling problems. A wide range of solved examples is used to illustrate these technique and how they can be put together to make the best possible risk-based decisions.The third edition of this highly regarded text has been thoroughly updated and expanded considerably with five new chapters for the risk manager, including how to plan and assess the quality of risk analysis, as well as new chapters for this risk analysis, as well as new chapters for the risk analysis modeller on summation of random variables, causality, optimization, insurance and finance modelling, forecasting, model validation and common errors, capital investment and microbial risk assessment. This new edition provides a greater focus on business and includes applications in a wide range of different settings.Key Features:Breaks down techniques into types of modelling issues (like distribution fitting, correlation and time series forecasting) and then applies them with easy-to-follow examples.Explains powerful and proven Monte Carlo simulation and numerical techniques for dealing with uncertainty.Includes recent innovations in modelling like fast Fourier transforms and copulas.Over 150 examples models and over 400 illustrations.Written in an informal manner with a practical rather than academic focus.Discusses the planning, uses and abuses of risk analysis.Includes a compendium of almost eighty distribution types and their uses.