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  • Bayesian statistics: As explained in Webinar 2, the field of Bayesian statistics is large, and rapidly evolving. This topic provides an overview of some of the key Bayesian concepts and techniques, including a number of @RISK example models. 
  • Financial risk models and examples: This topic contains links to a number of example financial risk models.
  • Using expert opinion in modeling: Here, we discuss various aspects of the use of expert opinion in probabilistic modeling, including potential sources of errors / biases. 

Online model Model on the interpretation of a diagnostic test: 


During the webinar we discussed the interactive model below (also found at


). This model uses Bayes' theorem to calculate the confidence in individual test results given a certain probability of infection prior to running the test (labeled Prevalence here), the probability of a positive test if the individual is infected (test Sensitivity), and the probability of observing a negative test given that the individual is not infected (test Specificity).  For example, if the pre-test probability of COVID-19 infection was 10%, and one uses a rapid test with 50% sensitivity and 95% specificity, we can say that we are roughly 95% sure that the individual is not infected if the test result is negative. Likewise, we would be almost 53% confident that the person is infected if the test is other words, under this scenario a positive test is as predictive as tossing a coin!

Iframe Macro