Risk Modeling and Decision Analysis

New York Oct 27-28, 2010

Part 1 - Introduction to Risk Analysis

• Background of risk analysis and risk management
• Risk analysis as a team effort
• Going from data to knowledge to a useful decision tool
• Dealing with the limits of current knowledge

Part 2 - Introduction to Statistical Descriptors

• Mean, mode, standard deviation, skewness, kurtosis, percentiles

Part 3 - Introduction to Probability Theory

• The use of distributions: uncertainty, variability and inter-individual variability
• Probability concepts
• Graphical representations of risk events: Venn diagrams, fault trees and event trees
• A look at some simple probability distributions

Part 4 - Introduction to Risk Modeling

• Monte Carlo simulation, Crystal Ball/@RISK/ModelRisk and Excel
• Brief tutorial on Crystal Ball/@RISK/ModelRisk
• Calculation vs. simulation - the pros and cons of Monte Carlo
• Typical risk analysis results, their presentation and interpretation
• Practical problems to solve
• The most common probability distributions

Part 5 - Stochastic Processes - the basis of risk analysis

• Binomial Process
• Binomial, beta, negative binomial and geometric distributions
• Imperfect tests, machine failures, risk events, etc.;
• Poisson Process
• Poisson, gamma, and exponential distributions
• Modeling insurance claims, accidents, random outbreaks, etc.

Part 6 - Hypergeometric Process

• Hypergeometric and inverse Hypergeometric distributions
• Survey results, prevalence estimate with imperfect diagnostic test, gambling, etc.

Part 7 - Practical Problems to Solve

Part 8 - Best Practices in Risk Modeling,
Common mistakes and how to prevent them,
Introduction to analyzing and using data for risk analysis

• Statistical techniques
• Why we need uncertainty distributions not confidence intervals in risk analysis
• Creating uncertainty distributions with standard Classical Statistical tests t-tests, z-tests, Chi-squared tests
• Examples of estimation of population mean and standard deviation
• The Bootstrap to include uncertainty
• The use of Bayesian Statistics in risk analysis

Part 9 - Example Risk Analyses

(a range of examples will also be covered during the course)

Part 10 - Wrap-up and review of course material

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