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  3. Bayesian Data Analysis in Python

Exercise

Decision analysis: cost

Your journey in marketing continues. You have already calculated the posterior click rates for clothes and sneakers ads, available in your workspace as clothes_posterior and sneakers_posteriors, respectively. Your boss, however, is not interested in the distributions of click rates. They would like to know what would be the cost of rolling out an ad campaign to 10'000 users. The company's advertising partner charges $2.5 per click on a mobile device and $2 on a desktop device. Your boss is interested in the cost of the campaign for each product (clothes and sneakers) on each platform (mobile and desktop): four quantities in total.

Let's compare these four posterior costs using the forest plot from pymc3, which has been imported for you as pm.

Instructions 1/4

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  • Calculate the distributions of the numbers of clicks for clothes and sneakers and assign the results to clothes_num_clicks and sneakers_num_clicks, respectively.