The correlated dice

This jupyter notebook aims to showcase a concise and memorable model system exhibiting temporal correlation in the obtained time series. In this model a dice is more probable to yield the same result as before than to yield a different result.

For the Markov chain at hand, we compute a numerical estimate of the auto correlation function as well as numerical auto correlation function from the given transition probabilities.

You can execute the notebook using binder However, you need to set "%matplotlib inline" disabling interactive plots.

Formatting of the notebook is enforced after running "pre-commit install" which will start black_nbconvert on every commit.