Update docs on tilde - what is a random variable and what is data

Consider the model

@model function inference_model(observed_data, params)
   a, b, c = observed_data
   ...
end

Passing in a tuple for observed data

a = some_vector_a
b = some_vector_b
c = some_vector_c
observed_data = (a, b, c)

_m1 = inference_model(observed_data, params)

generates error

nested task error: ArgumentError: Some indices in the output vector were not set. This likely means that the vector values provided are not consistent with the LogDensityFunction (e.g. if they were obtained from a different model).

However, it works fine if you pass in the data individually

@model function inference_model(a, b, c, params)
   ...
end
a = some_vector_a
b = some_vector_b
c = some_vector_c
_m1 = inference_model(a, b, c, params)

From an old Slack thread (which might not be available to you past 90 days), penelope provides an explanation:

Oh, actually, never mind. I see why. The issue is that Turing looks at your argument names to see what is a random variable and what is data.
If you have x ~ dist, and x is an argument then it will be treated as data. Otherwise it will be a random variable (even if x is part of some larger aggregate that is an argument ).

It was then suggested in the Slack thread to make the docs a little bit more clear on this. I am just creating this issue so that it is tracked, and plan on submitting a PR as soon as possible.

Start by finding the documentation section that explains @model arguments and the x ~ distribution syntax. Use the tuple and individual-argument examples from the issue to clarify how Turing distinguishes random variables from observed data; done means the behavior and workaround are understandable without relying on the Slack thread.