given a Turing model
@model function λ(y) λ₁ ~ Exponential(alpha) for i in 1:length(y) y[i] ~ Poisson(λ₁) end end
what is the meaning of the y array?
I think it is used to compute the posterior.
When I call a sampler, y will be the data. So in the model strucutre the data is rewrite by the distribution?
Question 0: How does it works exactly?
Question 1: How can I plot the y distribution?
Calling plot(sample(myModel(data), sampler(), n)) does not plot y.
Using PyMC3 the posterior distribution can be plotted as described here:
(figure at “4. Plot the artificial dataset:”)
Question 2: Can I use map/broadcast instead of
for i in 1:length(y) y[i] ~ Poisson(λ₁) end