I’m following the tutorial at https://docs.pymc.io/notebooks/Bayes_factor.html using Pyro and would like to clarify something for my own understanding.

From the following cell in PyMC3

```
import pymc3 as pm
import numpy as np
y = np.repeat([1, 0], [50, 50]) # 50 "heads" and 50 "tails"
priors = ((1, 1), (30, 30))
n_chains = 1000
models = []
traces = []
for alpha, beta in priors:
with pm.Model() as model:
a = pm.Beta('a', alpha, beta)
yl = pm.Bernoulli('yl', a, observed=y)
trace = pm.sample(1000, step=pm.SMC(), random_seed=42)
models.append(model)
traces.append(trace)
BF_smc = models[1].marginal_likelihood / models[0].marginal_likelihood
print(round(BF_smc))
```

I’ve converted the above tutorial code to the following in Pyro:

```
import numpy as np
import pyro
import pyro.distributions as dist
from pyro.infer import MCMC, NUTS
import torch
def model(y, alpha, beta):
a = pyro.sample('a', dist.Beta(alpha, beta))
yl = pyro.sample('yl', dist.Bernoulli(a), obs=y)
return yl
y = np.repeat([1, 0], [50, 50]) # 50 "heads" and 50 "tails"
priors = ((1, 1), (30, 30))
y_tensor = torch.from_numpy(y)
models = []
traces = []
for alpha, beta in priors:
nuts_kernel = NUTS(model)
mcmc = MCMC(nuts_kernel, num_samples=1000, warmup_steps=200)
mcmc.run(y_tensor, alpha, beta)
models.append(mcmc)
mcmc_samples = mcmc.get_samples()
traces.append(mcmc_samples)
```

My question is: What is the parallel in pyro for `models[1].marginal_likelihood`

? My ideas so far are to use something in `poutine.trace`

or perhaps some functionality in arviz? I feel that I’m missing something obvious here but extended searches of the docs and examples haven’t revealed anything to me so far. Thanks for the help!