Hi, Playing around with the classical problem of inferring the bias of a coin based on observed outcomes, I noticed the strange behavior for extreme observations. Imagine we toss 30 times a coin and we get only heads (ones). We model the problem as a Bernoulli distribution with a flat prior for p …

Agreed with @ricardoV94 , that you are seeing beta(2,1). Not sure why (it’s as if np.ones(30) is being treated as if it’s np.array([1]) instead), but if you wrap your numpy array in a pm.Data object, it seems to recognize all 30 flips, even when they are all heads: with pm.Model() as coin_flipping:…

Bernouilli corner case, all heads coin?

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cluhmann December 27, 2020, 8:56pm 5

I went ahead and opened an issue here.

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Bernouilli corner case, all heads coin?

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