# Why can't I use a Bernoulli as a likelihood variable in a hierarchical model in PyMC3?

This is essentially the "Multiple Coins from Multiple Mints / Baseball Players" example from Doing Bayesian Data Analysis, Second Edition (DBDA2). I believe I have PyMC3 code which is functionally equivalent, but one works and the other does not. This is with PyMC3 version 3.5. In more detail,

Let's say I have the following data. Each row is an observation:

observations_dict = {
'mint': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
'coin': [0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4, 4, 4, 5, 5, 6, 6, 7],
'outcome': [1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1]
}
observations = pd.DataFrame(observations_dict)
observations


### One Mint, Several Coins

The below, which implements DBDA2 Figure 9.7, runs just fine:

num_coins = observations['coin'].nunique()
coin_idx = observations['coin']

with pm.Model() as hierarchical_model:
# mint is characterized by omega and kappa
omega = pm.Beta('omega', 1., 1.)
kappa_minus2 = pm.Gamma('kappa_minus2', 0.01, 0.01)
kappa = pm.Deterministic('kappa', kappa_minus2 + 2)

# each coin is described by a theta
theta = pm.Beta('theta', alpha=omega*(kappa-2)+1, beta=(1-omega)*(kappa-2)+1, shape=num_coins)

# define the likelihood
y = pm.Bernoulli('y', theta[coin_idx], observed=observations['outcome'])


### Many Mints, Many Coins

However, once this is turned into a hierarchical model (as seen in DBDA2 Figure 9.13), it fails:

num_mints = observations['mint'].nunique()
mint_idx = observations['mint']
num_coins = observations['coin'].nunique()
coin_idx = observations['coin']

with pm.Model() as hierarchical_model2:
# Hyper parameters
omega = pm.Beta('omega', 1, 1)
kappa_minus2 = pm.Gamma('kappa_minus2', 0.01, 0.01)
kappa = pm.Deterministic('kappa', kappa_minus2 + 2)

# Parameters for mints
omega_c = pm.Beta('omega_c',
omega*(kappa-2)+1, (1-omega)*(kappa-2)+1,
shape = num_mints)
kappa_c_minus2 = pm.Gamma('kappa_c_minus2',
0.01, 0.01,
shape = num_mints)
kappa_c = pm.Deterministic('kappa_c', kappa_c_minus2 + 2)

# Parameters for coins
theta = pm.Beta('theta',
omega_c[mint_idx]*(kappa_c[mint_idx]-2)+1,
(1-omega_c[mint_idx])*(kappa_c[mint_idx]-2)+1,
shape = num_coins)

y2 = pm.Bernoulli('y2', p=theta[coin_idx], observed=observations['outcome'])


The error is:

ValueError: operands could not be broadcast together with shapes (8,) (20,)


as the model has 8 thetas for 8 coins but sees 20 rows of data.

However, if the data is grouped such that each line represents the final statistics of an individual coin, as with the following

grouped = observations.groupby(['mint', 'coin']).agg({'outcome': [np.sum, np.size]}).reset_index()
grouped.columns = ['mint', 'coin', 'heads', 'total']


And the final likelihood variable is changed to a Binomial, as follows

num_mints = grouped['mint'].nunique()
mint_idx = grouped['mint']
num_coins = grouped['coin'].nunique()
coin_idx = grouped['coin']

with pm.Model() as hierarchical_model2:
# Hyper parameters
omega = pm.Beta('omega', 1, 1)
kappa_minus2 = pm.Gamma('kappa_minus2', 0.01, 0.01)
kappa = pm.Deterministic('kappa', kappa_minus2 + 2)

# Parameters for mints
omega_c = pm.Beta('omega_c',
omega*(kappa-2)+1, (1-omega)*(kappa-2)+1,
shape = num_mints)
kappa_c_minus2 = pm.Gamma('kappa_c_minus2',
0.01, 0.01,
shape = num_mints)
kappa_c = pm.Deterministic('kappa_c', kappa_c_minus2 + 2)

# Parameter for coins
theta = pm.Beta('theta',
omega_c[mint_idx]*(kappa_c[mint_idx]-2)+1,
(1-omega_c[mint_idx])*(kappa_c[mint_idx]-2)+1,
shape = num_coins)

y2 = pm.Binomial('y2', n=grouped['total'], p=theta, observed=grouped['heads'])


Everything works. Now, the latter form is more efficient and generally preferred, but I believe the former should work as well. So I believe this is primarily a PyMC3 issue (or even more likely, a user error).

To quote DBDA Edition 1,

"The BUGS model uses a binomial likelihood distribution for total correct, instead of using the Bernoulli distribution for individual trials. This use of the binomial is just a convenience for shortening the program. If the data were specified as trial-by-trial outcomes instead of as total correct, then the model could include a trial-by-trial loop and use a Bernoulli likelihood function"

What bothers me is that in the very first example (One Mint, Several Coins), it looks like PyMC3 can handle individual observations instead of aggregated observations just fine. So I believe the first form should work, but doesn't.

### Code

http://nbviewer.jupyter.org/github/JWarmenhoven/DBDA-python/blob/master/Notebooks/Chapter%209.ipynb

### References

https://stackoverflow.com/questions/46952953/pymc3-differences-in-ways-observations-are-passed-to-model-difference-in-re

https://discourse.pymc.io/t/pymc3-differences-in-ways-observations-are-passed-to-model-difference-in-results/501

http://www.databozo.com/deep-in-the-weeds-complex-hierarchical-models-in-pymc3

Is this correct hierarchical Bernoulli model?

• any definition/explanation for "a likelihood variable" in teh title? Oct 6, 2018 at 3:31
• Sure, I just mean the very last line of the model declarations in the with statements. So, the y = pm.Bernoulli('y', theta[coin_idx], observed=observations['outcome']) line in the first example, the y2 = pm.Bernoulli('y2', p=theta[coin_idx], observed=observations['heads']) line in the second example, and the y2 = pm.Binomial('y2', n=grouped['total'], p=theta, observed=grouped['heads']) line in the third example. All three define the likelihood of the data, given the parameter theta. Sorry if I used the wrong language! Oct 6, 2018 at 4:39

## 1 Answer

The length of mint_idx was 20 (one for each observation), but it should have been 8 (one for each coin).

Working answer, notice the mint_idx recalculation (rest remains the same):

grouped = observations.groupby(['mint', 'coin']).agg({'outcome': [np.sum, np.size]}).reset_index()
grouped.columns = ['mint', 'coin', 'heads', 'total']

num_mints = grouped['mint'].nunique()
mint_idx = grouped['mint']
num_coins = observations['coin'].nunique()
coin_idx = observations['coin']

with pm.Model() as hierarchical_model2:
# Hyper parameters
omega = pm.Beta('omega', 1, 1)
kappa_minus2 = pm.Gamma('kappa_minus2', 0.01, 0.01)
kappa = pm.Deterministic('kappa', kappa_minus2 + 2)

# Parameters for mints
omega_c = pm.Beta('omega_c',
omega*(kappa-2)+1, (1-omega)*(kappa-2)+1,
shape = num_mints)
kappa_c_minus2 = pm.Gamma('kappa_c_minus2',
0.01, 0.01,
shape = num_mints)
kappa_c = pm.Deterministic('kappa_c', kappa_c_minus2 + 2)

# Parameters for coins
theta = pm.Beta('theta',
omega_c[mint_idx]*(kappa_c[mint_idx]-2)+1,
(1-omega_c[mint_idx])*(kappa_c[mint_idx]-2)+1,
shape = num_coins)

y2 = pm.Bernoulli('y2', p=theta[coin_idx], observed=observations['outcome'])