Hey @Rubenmak I just went through the talk and I found it incredibly helpful in my understanding of hierarchical models. Thank you for putting it together.
I did have one question about the model.
In each of the models you had something to the degree of:
p = sum(beta[i] * x_hat[:, i] for i in range(max_cap))
I’m wondering why you would sum these values? I’m trying to reason about this logic, as x_hat is is just the proportion of users who saw a given number of impressions for all the cap levels. So how would the sum of those values be helpful for the analysis? As would would we not want to just pass in the raw proportion values, not the sum of their proportions?
Thank you again for your work!