How can we asses risks with small data sets? One of the challenges in insurance is that despite having many customers, insurance companies often have only small amounts of claims data with which they can assess risks.
Markus Gesmann will present some Bayesian ideas to analyse risks with little, or even no event data.
We will touch on ideas from Daniel Kahneman and David Spiegelhalter and play around with Bayesian Belief Networks, hierarchal models and Stan.
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