I want to extract the estimated parameters from distributional models via Bayesian model stacking. I am using **pp_average** to calculate the posterior predictive values averaged across models. But it seems that I can only get the predicted values for **y** rather than **sigma** from the stacked model. The simplified model is attached below.

Model 1:

```
y ~ x1
sigma ~ x1
```

Model 2:

```
y ~ x2
sigma ~ x2
```

```
pp_average(model1, model2, weights = 'stacking')
```

I am wondering whether it is also possible to get the predicted values for **sigma**, and extract the estimated parameters for **x1** and **x2** from the stacked model in brms. Thanks a lot!