A statistical learning exercise based on a modified Rock-Paper-Scissors game

The standard version of the game Rock-Paper-Scissors is interesting in terms of game theory, but less so in terms of Statistics. However, we show that with a small rule change it can be made into an interactive exercise for degree-level students of Statistics that leads to a Bayesian change-point model, for which the Gibbs sampler provides an intuitive method of inference. First, students play the game to generate the data. Second, they are encouraged to formulate a model that reflects their experience from having played the game. And third, they participate in the development of a suitable MCMC algorithm to fit the model.