Hierarchical Modelling in Stan: Predicting the Premier League

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In this video, we will see how to implement a hierarchical model in Stan applied to the outcomes of the premiere league 19/20 season football matches. The hierarchical method will be compared to non-hierarchical methods to highlight the benefits in using this approach.
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Extra reading:
https://betanalpha.github.io/assets/case_studies/divergences_and_bias.html
https://jrnold.github.io/bayesian_notes/multilevel-models.html
https://mc-stan.org/users/documentation/case-studies/radon.html
The model used here is loosely based from:
https://discovery.ucl.ac.uk/id/eprint/16040/1/16040.pdf
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@mcmc_stan
Links:
Stan website: https://mc-stan.org
Stan community: https://discourse.mc-stan.org
Support Stan: https://mc-stan.org/support/
Github link to code & model: https://github.com/MaggieLieu/STAN_tutorials/tree/master/Hierarchical
Premiere league 19/20 data adapted from:
https://github.com/openfootball/england
Rstan installation:
https://youtu.be/4t6niM6sksI
Pystan installation:
https://youtu.be/YtR18hdAWmU

Видео Hierarchical Modelling in Stan: Predicting the Premier League канала Stan