Hello,

I im trying out different methods to estimate my latent variables. The variables should be correlated and hence I want to use an AutoNormalGuide to get the means as well as the variance and co-variance. Now since the usage of AutoNormal guide seems to hides what parameter is being estimated (because all parameters are contained in auto_loc, auto_scale) I do not know which parameter is represented by a certain index. As an example:

PS using the diagonal guide for now to just get started without too much comp effort

```
de model():
alpha = pyro.sample('alpha', dist.Gamma(7.0, 1.0))
beta = pyro.sample('beta', dist.Gamma(7.0, 1.0))
noise_std = pyro.sample('noise_std', dist.Normal(0.0, 0.1))
raw_data = pyro.sample('raw_data', dist.InvGamma(alpha, beta).expand(1000))
obs_data = pyro.sample('obs', dist.Normal(raw_data, noise_std))
conditioned_model = pyro.condition(model, data={"obs_data": data})
guide = AutoDiagonalNormal(conditioned_model)
```

In the above model, at which index of auto_loc, auto_scale do I find the est mean and scale of noise_std for example.

A second question I have is how the parameter for ‘raw_data’, which is sampled from a distribution dependent on alpha and beta, would be represented in the guide for this model. I want to find the estimated locs as well as the scales of the entries contained in the raw_data tensor. Since raw_data is a sample itself I don’t know how to represent its parameter this in the guide.

Thanks in advance!

Abel