Update advanced.jmd

tutorials/docs-09-using-turing-advanced/advanced.jmd

@@ -121,7 +121,7 @@ prior of your model but only when computing the log likelihood and the log joint
 should [check the type of the internal variable `__context_`](https://github.com/TuringLang/DynamicPPL.jl/issues/154)
 such as
 
-```{julia; eval=false}
+```julia; eval=false
 if DynamicPPL.leafcontext(__context__) !== Turing.PriorContext()
     Turing.@addlogprob! myloglikelihood(x, μ)
 end
@@ -176,6 +176,59 @@ gdemo(x) = Turing.Model(gdemo, (; x))
 model = gdemo([1.5, 2.0])
 ```
 
+### Reparametrization and generated_quantities
+
+Often, the most natural parameterization for a model is not the most computationally feasible. Consider the following
+(efficiently reparametrized) implementation of Neal's funnel [(Neal, 2003)](https://arxiv.org/abs/physics/0009028):
+
+```julia; eval=false
+@model function Neal()
+    # Raw draws
+    y_raw ~ Normal(0, 1)
+    x_raw ~ arraydist([Normal(0, 1) for i in 1:9])
+
+    # Transform:
+    y = 3 * y_raw
+    x = exp.(y ./ 2) .* x_raw
+
+    # Return:
+    return [x; y]
+end
+```
+
+In this case, the random variables exposed in the chain (`x_raw`, `y_raw`) are not in a helpful form — what we're after is the deterministically transformed variables `x, y`.
+
+More generally, there are often quantities in our models that we might be interested in viewing, but which are not explicitly present in our chain.
+
+We can generate draws from these variables — in this case, `x, y` — by adding them as a return statement to the model, and then calling `generated_quantities(model, chain)`. Calling this function outputs an array of values specified in the return statement of the model.
+
+For example, in the above reparametrization, we sample from our model:
+
+```julia; eval=false
+chain = sample(Neal(), NUTS(), 1000)
+```
+
+and then call:
+
+```julia; eval=false
+generated_quantities(Neal(), chain)
+```
+
+to return an array for each posterior sample containing `x1, x2, ... x9, y`.
+
+In this case, it might be useful to reorganize our output into a matrix for plotting:
+
+```julia; eval=false
+reparam_chain = reduce(hcat, generated_quantities(Neal(), chain))'
+```
+
+Where we can recover a vector of our samples as follows:
+
+```julia; eval=false
+x1_samples = reparam_chain[:, 1]
+y_samples = reparam_chain[:, 10]
+```
+
 ## Task Copying
 
 Turing [copies](https://github.com/JuliaLang/julia/issues/4085) Julia tasks to