Even though we may not have any actual observations from a domain, we can still speculate and hypothesize about possible rare events, i.e., we can reason on theoretical grounds as to what could possibly go wrong.
The objective of this webinar is to present Bayesian networks as a framework to merge machine-learned knowledge from data with theoretical knowledge from domain experts to produce a joint probability distribution that includes common and rare events simultaneously.
The case study we present addresses some of the challenges of Modern Portfolio Theory and was inspired by Rebonato & Denev's book, Portfolio Management Under Stress.
Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan, which included assignments in North America, Europe, and Asia.
Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognized as a thought leader in applying Bayesian networks for research, analytics, and reasoning.