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  3. Bayesian Data Analysis in Python

Exercise

Sample from predictive density

Finally! Your job is to predict the number of bikes rented per day, and you are almost there. You have fitted the model and verified the quality of parameter draws. You have also chosen the better of the two competing models based on the WAIC. Now, it's time to use your best model to make predictions!

A couple of new observations, not seen by the model, have been collected in a DataFrame named bikes_test. For each of them, we know the true number of bikes rented, which will allow us to evaluate model performance. In this exercise, you will get familiar with the test data and generate predictive draws for every test observation. The trace of your model which you have generated before is available as trace_2, and pymc3 has been imported as pm. Let's make predictions!

Instructions 1/3

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  • Print the head of bikes_test to the console and get familiar with the data.