Grain

Hit and run. Think Bayes!

At the R in Insurance conference Arthur Charpentier gave a great keynote talk on Bayesian modelling in R. Bayes’ theorem on conditional probabilities is strikingly simple, yet incredibly thought provoking. Here is an example from Daniel Kahneman to test your intuition. But first I have to start with Bayes’ theorem. Bayes’ theoremBayes’ theorem states that given two events $D$ and $H$, the probability of $D$ and $H$ happening at the same time is the same as the probability of $D$ occurring, given $H$, weighted by the probability that $H$ occurs; or the other way round.

Predicting claims with a Bayesian network

Here is a little Bayesian Network to predict the claims for two different types of drivers over the next year, see also example 16.15 in [1]. Let’s assume there are good and bad drivers. The probabilities that a good driver will have 0, 1 or 2 claims in any given year are set to 70%, 20% and 10%, while for bad drivers the probabilities are 50%, 30% and 20% respectively. Further I assume that 75% of all drivers are good drivers and only 25% would be classified as bad drivers.