# Turing.jl - update variables within the model

I’m using Turing to fit a model, where every data point is a pair (S,L), as we describe in the following paper: https://www.biorxiv.org/content/10.1101/2020.02.13.947531v1

In practice, we do not use L to fit the model, but L-(S-1), and I have not been able to use this directly from within Turing - what I had to do was create a variable (equal to L.-(S.-1)) and then call the model on this.

The model is as follows:

``````@model flexible_links(s,f) = begin
N = length(s)
# Transformed data
F = (s.*s) .- (s .- 1.0)
# Parameters
ϕ ~ Normal(3.0, 0.5)
μ ~ Beta(3.0, 7.0)
for i in 1:N
f[i] ~ BetaBinomial(F[i], μ*exp(ϕ), (1-μ)*exp(ϕ))
end
return μ, ϕ
end
``````

What I would like to write instead would look something like this:

``````@model flexible_links(s,l) = begin
N = length(s)
# Transformed data
f = [l[i]-(s[i]-1) for i in 1:N]
F = (s.*s) .- (s .- 1.0)
# Parameters
ϕ ~ Normal(3.0, 0.5)
μ ~ Beta(3.0, 7.0)
for i in 1:N
f[i] ~ BetaBinomial(F[i], μ*exp(ϕ), (1-μ)*exp(ϕ))
end
return μ, ϕ
end
``````

But it does not work, because f is of the wrong type - I’d appreciate any points as to where I should be looking to solve the problem.

Turing assumes that arguments to the model are observations and that all other model parameters should be treated as parameters. If f should be considered an observation you need to pass it to the model definition. Your model will also train faster if you construct f outside.

As an alternative you could use the @logpdf macro. But I would refrain from using it if not necessary.

1 Like

Gotcha - so there is no way in Turing to emulate what stan does with transformed variables, as in https://github.com/PoisotLab/ms_straight_lines/blob/master/stan/betabin_connectance.stan#L6 ?

At the moment we don’t have a way to do this, mainly because we assume that observations are passed as arguments. Transformations itself should not be an issue if we would be able to track that `f` depends on `l` and `s`. At the moment we don’t have a handle on this but it could be added to the compiler.

Could you open an issue if you feel that this would be an useful feature?

– EDIT –

Thanks for trying to implement your model in Turing! 