My question is more about prediction but feel free to criticise how I’ve written my pyro code. I have the following linear model, of which I wish to put priors on weights and biases:

```
class Model(nn.Module):
def __init__(self, dims):
super().__init__()
self.linear = nn.Linear(dims, 1)
def forward(self, x):
return self.linear(x)
model = Model(1)
```

I was following this blog on how to convert NNets to pyro and get the following code:

```
def bayes_model(x_data, y_data):
# define priors
get_params = lambda w: (torch.zeros_like(w), torch.ones_like(w))
priors = {name:Normal(*get_params(w)) for name, w in
model.named_parameters()}
# lift onto a random pyro module
lifted_module = pyro.random_module("module", model, priors)
lifted_reg_model = lifted_module()
# define rest of model with likelihood
yhat = lifted_reg_model(x_data)
scale = pyro.sample("sigma", Uniform(0, 5))
pyro.sample("obs", Laplace(yhat, scale), obs=y_data)
return yhat
guide = AutoDiagonalNormal(bayes_model)
optim = Adam({"lr": 0.03})
svi = SVI(bayes_model, guide, optim, loss=Trace_ELBO(), num_samples=1000)
```

And I train the pyro model with:

```
pyro.clear_param_store()
epochs = 15
for _ in range(epochs):
loss = 0
for x_data, y_data in train_dl:
loss += svi.step(x_data, y_data)
print(f"loss: {loss / len(train_data):.4f}")
```

**The question is how do I predictive inference, now that I have a trained pyro model?**

A minimal working example is included in this colab notebook.

The following was my feeble attempt at doing predictive inference but I’m clearly doing it wrong:

```
num_samples = 10
def predict(x):
sampled_models = [guide(None, None) for _ in range(num_samples)]
yhats = [model(x).data for model in sampled_models]
return yhats
pred_y_train = predict(torch.Tensor(x))
```