What does the pymc3.Binomial function do when the parameter n receives a list of values?

When I define a pm.Binomial and the parameter n receives a list of values, does metropolis define n as a constant or a random variable? How does he behave? Because from my understanding, if n is a random variable, the space to be covered can be very large according to the problem. I couldn’t find this information anywhere.

For example :
observation = pm.Binomial(“obs”, p = p, n = list_blue_balls, observed= list_all_balls)
trace = pm.sample(18000,step=pm.Metropolis())

1 Like

Though are certainly other uses, I think the primary intention is so that you can model data that consist of multiple sequences, each of which has can (but doesn’t necessarily need) its own n, it’s own p, and in the case of an observed variable, it’s own k (number of successes).

flips = [0, 3, 6, 600]
successes = np.multiply(2/3, flips)

coords = {
    "coin": list(range(len(successes)))
with pm.Model(coords=coords) as model:
    ps = pm.Beta('ps',
    observation = pm.Binomial('like',
    idata = pm.sample(return_inferencedata=True)


This yields:

Dimensions:  (coin: 4)
  * coin     (coin) int64 0 1 2 3
Data variables:
    ps       (coin) float64 0.4996 0.5985 0.627 0.6666

Note that You can also assign a random variable to n - it behaves just as any random variable that the Binomial likelihood will conditioned on n (sample/proposal in the context of MCMC). There are some demonstrations in All-that-likelihood-with-PyMC3/Likelihood_visual_demo.ipynb at master · junpenglao/All-that-likelihood-with-PyMC3 · GitHub