Hi,

It’s my first pyro example code (I apologize if the question is oversimple) and I have difficulties in trying out the model with a fresh test sample.

## Data

I am working with NCAA® basketball games played between Division I women’s teams in 2017.

## Model

I am assuming that each team has a power score coming from a ~N(0,1) distribution and model the scores between home and away teams according to a Normal distribution with ~N(\text{home team power}-\text{away team power}, 10)

```
def normal_winner_model(home_index, away_index, score):
num_teams = teams_2017.shape[0]
num_games = home.shape[0]
mu = torch.zeros(num_teams ,1)
sigma = torch.ones(num_teams ,1)
prior_power = pyro.sample("prior_power", Normal(mu ,sigma))
maskhome = torch.zeros(num_teams ,num_games ,dtype=torch.float)\
.scatter_(0, home_index[None,:] , 1.)
maskaway = torch.zeros(num_teams ,num_games ,dtype=torch.float)\
.scatter_(0, away_index[None,:] , 1.)
score_mu = prior_power.transpose(0,1).matmul(maskhome - maskaway).squeeze()
with pyro.plate("data"):
pyro.sample("score",Normal(score_mu,10),obs = score)
```

## inference

I am using a MultivariateNormal `guide`

```
guide = AutoMultivariateNormal(normal_winner_model)
```

and perform SVI inference with Adam optimizer

## Testing the model

As I understand I need to replay the `guide`

's `prior_power`

parameter and feed the frozen model with the test data

```
preds = []
for _ in range(1000):
guide_trace = pyro.poutine.trace(guide).get_trace(hloc_test, aloc_test, None)
# assuming that the original model took in data as (x1, x2, y) where y is observed
lifted_model = pyro.poutine.replay(normal_winner_model, guide_trace)
preds.append(lifted_reg_model(hloc_test, aloc_test, None))
```

however all preds are `None`

. What do I miss here?

Thanks