Is there an elegant way to extract samples from the prior distributions in a pyro model? All the examples I’ve seen show the posterior samples using `MCMC().get_samples()`

, and posterior predictive and prior predictive samples using the `Predictive`

class?

if your model has return values you can directly call it and get those:

```
def model():
pyro.sample("z", ...)
x = pyro.sample("x", ...)
return x
x = model()
```

of course this will only get you the returned site(s). if you want all the sites, you can use `pyro.poutine.trace`

:

```
model_trace = pyro.poutine.trace(model).get_trace(model_args)
# inspect the structure of model_trace to pull out what you want, e.g.
for name, site in model_trace.nodes.items():
if site["type"] == "sample":
print(name, site["value"])
```

1 Like

You can also use `Predictive`

as a convenience utility to draw samples from the prior by passing an empty dict to `posterior_samples`

argument, which will essentially do what @martinjankowiak’s snippet above is doing. An additional advantage is that if all the batch dimensions are annotated correctly with `pyro.plate`

, you can use `parallel=True`

to draw a single vectorized sample which might be faster for more complex models.

```
def model(x, y=None):
...
pyro.sample('y', dist.Normal(0., 1.), obs=y)
# draw 100 samples from the prior
prior_samples = Predictive(model, {}, num_samples=100)(x)
print(prior_samples)
```