Simple way to making a prediction using PyMC model

Questions
3

Thank you for yours response!

Thank you also for your remark about Beta distribution, it definitely improves the model.

I would like to make sure - if I need the distibution of revenue, not just expected revenue, I can do the following:

# p.shape = (2000, 1)
p = idata.posterior['transaction_probability'].values.ravel()[:, np.newaxis]
# mu.shape = (2000, 1)
mu = idata.posterior['mu'].values.ravel()[:, np.newaxis]

# overall_revenue_samples.shape = (2000,)
overall_revenue_samples = (p * (mu + test['Price'].values[np.newaxis, :])).sum(axis=1)

Then, I can visualize overall_revenue_samples with, for example, arviz plot_posterior. Am I right?

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