Hi PyMC discourse,

This is a common example, but I found it in McElreath - measuring points on a globe to determine the proportion that is water (see code). I’d like to know how to expand this into a situation with multiple globes, which may have slightly different proportions of water. I’m looking to share information between the globes, though, to determine the expected overall proportion of water on *any* globe.

Single globe example:

```
import pymc3 as pm
#A value of 0 signifies a land observation, a value of 1 signifies a water observation
observations = [0, 0, 1, 0, 1]
water_observations = sum(observations)
total_observations = len(observations)
with pm.Model() as planet_model:
# Prior
p_water = pm.Uniform("p_water", 0 ,1)
# Likelihood
w = pm.Binomial("w", p=p_water, n=total_observations, observed=water_observations)
# Inference Run/ Markov chain Monte Carlo
trace_5_obs = pm.sample(5000, chains=2)
```

My first thought is to just change the shape parameters, but there are a large number of individual measurements - is this the best approach? As an example, in the multiple globes case, one might have:

observation_globe1 = [0, 0, 1, 0, 1]

observation_globe2 = [0, 0, 1, 0, 1, 0, 0, 1]

observation_globe3 = [0, 1]

…

observation_globe650000 = [1, 0, 0, 0, 1]

After this I may stratify into groups of globes and do a hierarchical run, but this is just the first step.

Thankyou!