Predicting elections in Paris with hierarchical multinomial regression.
Do you want to use multinomial/softmax regression but don’t know how to code that up? Well (at once), there is a tutorial about this! You’ll learn how to write these models with PyMC3 and ArviZ, using a non-trivial example: a hierarchical multinomial regression to predict city-council elections in Paris at the district-level, using opinion polls and unemployment data as predictors.
Beyond its originality, this example illustrates useful tricks when building Bayesian models: dealing with sparse and unreliable data, changes in electoral rules over time, multi-dimensionality, probability distributions that are not often used in tutorials but are harder to fit than your vanilla Gaussian, space and time variation, hierarchical structure, etc. As a bonus, we’ll visualize our results with a beautiful, interactive map of Paris districts, using Bokeh.
TL; DR: this is not yet another Iris data example — so come and watch!
By day, Alex is a data scientist and modeler at the brand new PyMC | Labs consultancy. By night, he doesn’t (yet) fight crime, but he’s an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. Alex is also the creator and host of the only podcast dedicated to Bayesian statistics, “Learning Bayesian Statistics”. Every fortnight, he interviews practitioners of all fields about why and how they use Bayesian statistics. He also loves Nutella a bit too much, but he doesn’t like talking about it – he prefers eating it.
This is a PyMCon 2020 talk
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