Use StableRNG, relax test tolerance

Author: mhauru

test/mcmc/gibbs.jl

@@ -16,6 +16,7 @@ using ForwardDiff: ForwardDiff
 using Random: Random
 using ReverseDiff: ReverseDiff
 import Mooncake
+using StableRNGs: StableRNG
 using Test: @inferred, @test, @test_broken, @test_deprecated, @test_throws, @testset
 using Turing
 using Turing: Inference
@@ -463,7 +464,10 @@ end
         # https://github.com/TuringLang/Turing.jl/issues/1725
         # sample(model, Gibbs(; z=MH(), m=HMC(0.01, 4)), 100);
         chn = sample(
-            model, Gibbs(; z=PG(10), m=HMC(0.01, 4; adtype=adbackend)), num_samples
+            StableRNG(23),
+            model,
+            Gibbs(; z=PG(10), m=HMC(0.01, 4; adtype=adbackend)),
+            num_samples,
         )
         # The number of m variables that have a non-zero value in a sample.
         num_ms = count(ismissing.(Array(chn[:, (num_zs + 1):end, 1])); dims=2)
@@ -476,7 +480,7 @@ end
         # the posterior is analytically known? Doing 10_000 samples to run the test suite
         # is not ideal
         # Issue ref: https://github.com/TuringLang/Turing.jl/issues/2402
-        @test isapprox(mean(num_ms), 8.6087; atol=0.5)
+        @test isapprox(mean(num_ms), 8.6087; atol=0.8)
         @test isapprox(std(num_ms), 1.8865; atol=0.02)
     end