Hi guys, Approved_Conversion Impressions interest 2 19 1727646 7 19 2622588 10 91 17989844 15 63 10745856 16 141 31799775 18 33 8646488 19 33 6083217 20 48 6899804 21 25 2833490 22 12 3965401 23 7 1836368 24 15 2256874 25 20 5251853 26 23 4868639 27 54 16352527 28 42 10959630 29 132 18768653 30 1…

Any suggestions to improve this model? Seems to be having trouble plotting the forest plot of ‘theta’

[image] springcoil: phi = pm.Uniform(‘phi’, lower=0.0, upper=0.001) The prior is really narrow - could be problematic.

I am not sure - maybe try doing a forward generation and check whether the generated data is in a reasonable range.

Can’t help you with that (new feature, haven’t used yet, see commits ~1 month ago) However: looking at your data, sometimes conversions > impressions, you need to cap that (but I presume you have) running this with a simple beta on the data above gives MAP of ~0.45, 0.3 for \alpha and \beta (…

Well interest is different segments on Facebook. I wanted to pool them for that reason. I’ll check the conversions etc. And make sure it’s capped. Mixture model could be interesting!

I don’t see in your code where you effectively pool. This would do it: ninterests = len(interests); with pm.Model() as ad_model: alphabeta = pm.HalfFlat('alphabeta', shape=(ninterests, 2)) probs = pm.Beta('probs', \ alphabeta[impressions['interests'].values(), 0], \ …

Cool I see where I went wrong with this. I’ll fix this up.

BTW, I think you could share \beta across groups, and pool \alpha's from a Gamma or exponential. Mathematically, with N the number of groups interests, and i = 0,…,N-1 being the group to which user j belongs, U the number of users and j = 0,…,U-1 : \theta = 1\\ \beta \sim HalfFlat()\\ \alpha_i \…

Problems with Hierachical Bayesian Model - Advertising

Questions

springcoil June 2, 2018, 3:42pm 6

I’m sorry how do I do a forward generation?

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Problems with Hierachical Bayesian Model - Advertising

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