I’m running into what I think might be a bug with PyMC3’s Binomial likelihood. Here’s the example. Simulate data itrs = 100 p = 0.4 W = [] L = [] N = [] for i in range(itrs): n = int(np.random.normal(loc=100, scale=20)) wins = np.random.binomial(n=n,p=p) losses = n - wins N.append(n) …

I am not sure where did I make a typo. A beta-binomial distribution or model refers to this kind of model where each observation is modelled with a binomial likelihood whose p parameter is in turn given a unique beta prior that has shared alpha/beta hyper priors. This: with pm.Model() as m1: alp…

Beta-Binomial conjugate prior -- pm.Binomial buggy results...?

Questions

jbuddy_13 November 3, 2021, 9:26pm 5

Just to be clear, you’re referring to pm.BetaBinomial(), right?

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Beta-Binomial conjugate prior -- pm.Binomial buggy results...?

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