This repo contains the materials for my PyMCon talk, Learning Bayesian Statistics with Pokemon GO.
Notebooks: these are cleaned up versions of what I live coded in the talk. They also include snippets of code (mostly plotting!) that didn't make it into the final video.
1_rayquaza.ipynb: suppose we got 2 shinies in 44 encounters; what do we think the shiny rate is?2_silph.ipynb: given a sequence of events, how does our understanding of the shiny rate change over time?3_dragon_week.ipynb: did the chance of hatching a Deino change in the middle of the Dragon Week event?
Data: There are two CSV files: rates.csv for the second model and dragon_week.csv for the third.
Other: The environment.yml file can reproduce my conda environment in which I ran all of the code.
Finally, find my slides here.