Risk Analysis: A Quantitative Guide

摘要: 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. Management Options. 1.4. Evaluating 1.5. Inefficiencies in Transferring Risks to Others. 1.6. Registers. 2. Planning analysis. 2.1. Questions and Motives. 2.2. Determine the Assumptions that are Acceptable or Required. 2.3. Time Timing. 2.4. You'll Need Good Analyst Team. 3. quality of 3.1. Reasons can be Terrible. 3.2. Communicating Quality Data Used Analysis. 3.3. Level Criticality. 3.4. Biggest Uncertainty 3.5. Iterate. 4. Choice model structure. 4.1. Software Tools Models they Build. 4.2. Calculation Methods. 4.3. Variability. 4.4. How Monte Carlo Simulation Works. 4.5. Modelling. 5. Understanding using results 5.1. Writing Report. 5.2. Explaining Model's Assumptions. 5.3. Graphical Presentation Results. 5.4. Statistical Methods Analysing 2: 6. Probability mathematics simulation. 6.1. Distribution Equations. 6.2. Definition "Probability". 6.3. Rules. 6.4. Measures. 7. Building running model. 7.1. Model Design Scope. 7.2. Easy Check Modify. 7.3. Efficient. 7.4. Most Common Modelling Errors. 8. Some basic random processes. 8.1. 8.2. Binomial 8.3. Poisson 8.4. Hypergeometric 8.5. Central Limit Theorem. 8.6. Renewal Processes. 8.7. Mixture Distributions. 8.8. Martingales. 8.9. Miscellaneous Example. 9. statistics. 9.1. Classical Statistics. 9.2. Bayesian Inference. 9.3. Bootstrap. 9.4. Maximum Entropy Principle. 9.5. Which Technique Should You Use? 9.6. Adding uncertainty Simple Linear Least-Squares Regression 10. Fitting distributions data. 10.1. Properties Observed Data. 10.2. Non-Parametric 10.3. First-Order Parametric 10.4. Second-Order 11. Sums variables. 11.1. Basic Problem. 11.2. Aggregate 12. Forecasting with uncertainty. 12.1. Series Forecast. 12.2. Financial Models. 12.3. Autoregressive 12.4. Markov Chain 12.5. Birth Death 12.6. Projection Events Occurring Randomly Time. 12.7. Leading Indicators. 12.8. Comparing Fits for Different 12.9. Long-Term Forecasting. 13. correlation dependencies. 13.1. 13.2. Rank Order Correlation. 13.3. Copulas. 13.4. Envelope Method. 13.5. Multiple Correlation Using Look-Up Table. 14. Eliciting expert opinion. 14.1. 14.2. Sources Error Subjective Estimation. 14.3. Techniques. 14.4. Calibrating Subject Matter Experts. 14.5. Conducting Brainstorming Session. 14.6. Interview. 15. Testing modelling causal relationships. 15.1. Campylobacter 15.2. Types Analyse 15.3. From Factors Causes. 15.4. Evidence. 15.5. Limits Causal Arguments. 15.6. An Example Qualitative 15.7. Is Essential? 16. Optimisation 16.1. 16.2. 16.3. Optimisation. 16.4. Working Example: Optimal Allocation Mineral Pots. 17. Checking validating 17.1. Spreadsheet 17.2. Behaviour. 17.3. Predictions Against Reality. 18. Discounted cashflow modelling. 18.1. Useful Sales Market Size. 18.2. Summing Random Variables. 18.3. Variable Margins Revenues. 18.4. Measures 19. Project 19.1. Cost 19.2. Schedule 19.3. Portfolios risks. 19.4. Cascading Risks. 20. Insurance finance analysis 20.1. Operational 20.2. Credit Risk. 20.3. Ratings 20.4. Other Areas 20.5. 20.6. Term Life Insurance. 20.7. Accident 20.8. Correlated Portfolio. 20.9. Extremes. 20.10. Premium Calculations. 21. Microbial food safety assessment. 21.1. Growth Attenuation 21.2. Dose-Response 21.3. Right Approach? 21.4. Simplifications. 22. Animal import 22.1. an Infected Animal. 22.2. Estimating True Prevalence Population. 22.3. Importing Problems. 22.4. Confidence Detecting Group. 22.5. Health Food Safety I. Guide lecturers. II. About ModelRisk. III. A compendium distributions. III.1. Discrete Continuous III.2. Bounded Unbounded III.3. III.4. Univariate Multivariate III.5. Lists Applications III.6. Read III.7. III.8. Introduction Creating Your Own III.9. Approximation One Another. III.10. Recursive Formulae III.11. Visual Observation On Behaviour Of IV. Further reading. V. Vose Consulting. References. Index.