Let’s say I have a simple Turing model

```
@model function model()
C ~ LKJ(3, 1.0)
x ~ MvNormal(zeros(3), C)
end
```

and obtained some posterior samples via MCMC

```
chain = sample(model(), NUTS(), 100)
```

or variational inference

```
q = vi(model(), ADVI(10, 1_000))
samples = rand(q, 100)
```

Is there an (easy) way to transform such samples into a vector of named tuples, i.e., `[(C = Matrix, x = Vector), (C = Matrix, x = Vector), ...]`

corresponding to the sampled random variables of the Turing model?

Could not find it in the documentation (hope to have just missed it).