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2 changes: 1 addition & 1 deletion Project.toml
Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,7 @@ TuringOptimExt = "Optim"
ADTypes = "1.9"
AbstractMCMC = "5.5"
Accessors = "0.1"
AdvancedHMC = "0.3.0, 0.4.0, 0.5.2, 0.6"
AdvancedHMC = "0.3.0, 0.4.0, 0.5.2, 0.6, 0.7"
AdvancedMH = "0.8"
AdvancedPS = "0.6.0"
AdvancedVI = "0.2"
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2 changes: 1 addition & 1 deletion test/mcmc/Inference.jl
Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ using Turing
)
check_gdemo(chn3)

chn3_contd = sample(StableRNG(seed), gdemo_default, alg3, 2_000; resume_from=chn3)
chn3_contd = sample(StableRNG(seed), gdemo_default, alg3, 5_000; resume_from=chn3)
check_gdemo(chn3_contd)
end

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4 changes: 2 additions & 2 deletions test/mcmc/gibbs.jl
Original file line number Diff line number Diff line change
Expand Up @@ -573,7 +573,7 @@ end
# is not ideal
# Issue ref: https://github.com/TuringLang/Turing.jl/issues/2402
@test isapprox(mean(num_ms), 8.6087; atol=0.8)
@test isapprox(std(num_ms), 1.8865; atol=0.02)
@test isapprox(std(num_ms), 1.8865; atol=0.03)
end

# The below test used to sample incorrectly before
Expand Down Expand Up @@ -655,7 +655,7 @@ end
# Run the Gibbs sampler and NUTS on the same model, compare statistics of the
# chains.
@testset "comparison with 'gold-standard' samples" begin
num_iterations = 1_000
num_iterations = 2_000
thinning = 10
num_chains = 4

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4 changes: 2 additions & 2 deletions test/mcmc/hmc.jl
Original file line number Diff line number Diff line change
Expand Up @@ -172,7 +172,7 @@ using Turing

@testset "nuts inference" begin
alg = NUTS(1000, 0.8; adtype=adbackend)
res = sample(StableRNG(seed), gdemo_default, alg, 500)
res = sample(StableRNG(seed), gdemo_default, alg, 5_000)
check_gdemo(res)
end

Expand Down Expand Up @@ -251,7 +251,7 @@ using Turing
gdemo_default_prior = DynamicPPL.contextualize(
demo_hmc_prior(), DynamicPPL.PriorContext()
)
chain = sample(gdemo_default_prior, alg, 500; initial_params=[3.0, 0.0])
chain = sample(gdemo_default_prior, alg, 5_000; initial_params=[3.0, 0.0])
check_numerical(
chain, [:s, :m], [mean(truncated(Normal(3, 1); lower=0)), 0]; atol=0.2
)
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