No matter how hard I look, I don’t see any difference between these two models, the programs of which are reproduced below:

import numpy as np

import pymc as pm

import arviz as azobserved_data = np.array([1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0,

1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0,

1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,

1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0,

1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,

1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1,

0, 1, 0, 1, 1, 0, 1])

**First:**

“”"

Bernoulli model

“”"

with pm.Model() as coin_flip_Bern:`prior_p = pm.Beta("p", alpha = 1, beta = 1) obs_data = pm.MutableData("observed", observed_data) likelihood = pm.Bernoulli("likelihood", p = prior_p, observed = obs_data) idata_Bern = pm.sample()`

**Second:**

“”"

Binomial model

“”"

with pm.Model() as coin_flip_Bin:`prior_p = pm.Beta("p", alpha = 1, beta = 1) obs_data = pm.MutableData("observed", observed_data) likelihood = pm.Binomial("likelihood", n = 73, p = prior_p, observed = 46) idata_Bin = pm.sample()`

Do you know of any documents that explain why you should choose `pm.Bernoulli`

, or on the contrary choose `pm.Binomial`

, depending on the situation? For me, the web page PyMC is totally unclear…