inline rand(::Distributions.Uniform)

Note that, apart from being simpler code, Distributions.Uniform also
doesn't allow the lower and upper bounds to be exactly equal (but we
might like to keep that option open in DynamicPPL, e.g. if the user
wants to initialise all values to the same value in linked space).

Author: penelopeysm

src/contexts/init.jl

@@ -38,6 +38,8 @@ to unconstrained space, and then sampling a value uniformly between `lower` and
 If unspecified, defaults to `(lower, upper) = (-2, 2)`, which mimics Stan's
 default initialisation strategy.
 
+Requires that `lower <= upper`.
+
 # References
 
 [Stan reference manual page on initialization](https://mc-stan.org/docs/reference-manual/execution.html#initialization)
@@ -55,7 +57,7 @@ end
 function init(rng::Random.AbstractRNG, ::VarName, dist::Distribution, u::UniformInit)
     b = Bijectors.bijector(dist)
     sz = Bijectors.output_size(b, size(dist))
-    y = rand(rng, Uniform(u.lower, u.upper), sz)
+    y = u.lower .+ ((u.upper - u.lower) .* rand(rng, sz...))
     b_inv = Bijectors.inverse(b)
     x = b_inv(y)
     # 0-dim arrays: https://github.com/TuringLang/Bijectors.jl/issues/398