MODULE 1 Introduction to Bayesian Risk Assessment 3 day course June 13-15, 2013 Instructed by: Drs. Curtis Smith & Javier Sanchez This three-day course will introduce the concepts of probabilistic risk analysis (PRA) using a Bayesian framework. PRA is an analysis of the frequency and consequences of unwanted events in a system. This type of analysis relies on probabilistic (i.e., predictive) models and associated data. Because of PRA’s focus on low frequency scenarios, empirical data are often lacking. Bayesian inference techniques are useful in such situations because, unlike frequentist statistical methods, Bayesian techniques are able to incorporate nonempirical information. Furthermore, from a practical perspective, Bayesian techniques, which represent uncertainty with probability distributions, provide a ready framework for the propagation of uncertainties through the risk models, via Monte Carlo sampling. All the examples and exercises for this course will be using animal health and food safety problems and they will be developed using the statistical package WinBUGS (http://www.mrc-bsu.cam.ac.uk/bugs/winbugs/contents.shtml). The main instructor for this course will be Dr. Curtis Smith, one of the authors of the book entitled: “Bayesian Inference for Probabilistic Risk Assessment” (http://www.amazon.com/Bayesian-Inference-Probabilistic-Risk-Assessment/d...).