bayesian-bandit

created by Austin Poor

Using pystan to approach a simplified version of the multi-armed bandit problem, where the goal is to use bayesian modeling to predict the probability of success for an unknown process.

The notebook bayes-bandit.ipynb has the analysis code and the file bandit.py has a 1-armed-bandit class used by the notebook.

Run the Code

Binder

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Docker

There's an included docker-compose file, for running the notebook in a container, which can be run with the command:

$ docker-compose up