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

Paola Bortot, Stuart Coles


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.


mathematics education; Bayesian Statistics; change-point analysis; Gibbs sampler; teaching

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DOI: https://doi.org/10.21100/msor.v18i1.1032


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