Hi guys again.

I am checking out the model averaging example but it is written with pymc3.

I have adjusted the code after each sample of each of the example three models with e.g.

```
trace_0.extend(pm.sample_prior_predictive(samples=2000, random_seed=58))
pm.sample_posterior_predictive(trace_0, extend_inferencedata=True, random_seed=58)
pm.compute_log_likelihood(trace_0)
```

used the traces with arviz compare to obtain the weight list and instead of `pm.sample_posterior_predictive_w`

I am using

```
ppc_w = az.weight_predictions(
[trace_0,trace_1,trace_2],
weights=list(comp.weight.values)
)
```

as it is suggested in Ch.5 of Bayesian Analysis with Python (3rd Edition). Is this a proper adjustment?

But i got stuck in adjusting

```
mean_w = ppc_w["kcal"].mean()
hpd_w = az.hdi(ppc_w["kcal"].flatten())
```

How can i adjust above lines of code to my pymc version (5.16.2)?

Is there an example of model averaging with newer versions of pymc instead of pymc3?

Thanks in advance.