Hi, you can pass a `"num_samples"`

argument along with `"enumerate": "parallel"`

in `infer`

to tell `TraceEnum_ELBO`

to approximately marginalize a variable with importance samples from the prior:

```
# suppose this site appears only in the model
pyro.sample("varname", dist, infer={
"enumerate": "parallel", "expand": True, "num_samples": 10})
```

Caveats: first, this will only produce correct gradient estimates if `dist`

is fully reparametrized, i.e. has an `rsample`

method, though this is true of most common continuous distributions such as `Normal`

. Second, this will cause every *descendant* (not just every *child*, as in exact enumeration with `"expand": False`

) of `varname`

to have an extra dimension of size `num_samples`

in its `log_prob`

and sample tensors. If `varname`

doesn’t have many descendants, this probably isn’t a big deal. Finally, the same restrictions on model structure and model-guide alignment that apply to exact enumeration apply here (outlined in the enumeration tutorial).

Note that you can also take multiple local samples in the *guide* for a variable appearing in model and guide in the same way, in order to reduce ELBO estimator variance.

If you have many such variables (for example, a latent Markov chain), you can try setting `"expand": False`

, which will use an experimental Tensor Monte Carlo estimator that avoids propagating sample dimensions beyond each variable’s children at the cost of extra variance in the overall ELBO estimate. If you want to work with non-reparametrized distributions or experiment with a different IWAE-style ELBO, you could try the Tensor Monte Carlo lower bound `pyro.infer.TraceTMC_ELBO`

.