Hello,
I’m new to Turing.j,l and probabilistic programming in general.
While playing around with small example codes, I faced a behaviour I don’t understand about the conditioning syntax:
If I understand well, these two model definitions are equivalent:
@model function model1(x)
μ ~ Uniform(0, 2)
x ~ LogNormal(μ, 1)
end;
@model function model2()
μ ~ Uniform(0, 2)
x ~ LogNormal(μ, 1)
end;
model2(x) = model2() | (; x)
However, when evaluating the negative log likelihood (using syntax from source code here and there) for these two models I obtain different results:
v = 1.0
w = [1.0]
ctx = Turing.OptimizationContext(DynamicPPL.LikelihoodContext())
Turing.OptimLogDensity(model1(v), ctx)(w) # = 1.4189385332046727
Turing.OptimLogDensity(model2(v), ctx)(w) # = 2.112085713764618
When computing it by hand it seems that the first model is correct:
nll(μ) = -logpdf(LogNormal(μ, 1), v)
nll(w[1]) # = 1.4189385332046727
Am I missing something ?
I’ll get on it asap!