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MvNormalWishart node implementation #565

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Merged
wouterwln merged 3 commits into from
Nov 27, 2025
Merged

MvNormalWishart node implementation #565

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c4e980c
tests failing
esther-van-pelt Nov 5, 2025
7a53485
Implement `MvNormalWishart` node and `out` rule
esther-van-pelt Nov 27, 2025
e028206
make format
esther-van-pelt Nov 27, 2025
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1 change: 1 addition & 0 deletions src/nodes/predefined.jl
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Expand Up @@ -9,6 +9,7 @@ include("predefined/mv_normal_mean_precision.jl")
include("predefined/mv_normal_mean_scale_precision.jl")
include("predefined/mv_normal_weighted_mean_precision.jl")
include("predefined/mv_normal_mean_scale_matrix_precision.jl")
include("predefined/mv_normal_wishart.jl")
include("predefined/gamma.jl")
include("predefined/gamma_inverse.jl")
include("predefined/gamma_shape_rate.jl")
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1 change: 1 addition & 0 deletions src/nodes/predefined/mv_normal_wishart.jl
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@@ -0,0 +1 @@
@node MvNormalWishart Stochastic [out, (μ, aliases = [mean]), (W, aliases = [scale]), λ, ν]
1 change: 1 addition & 0 deletions src/rules/mv_normal_wishart/out.jl
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@rule MvNormalWishart(:out, Marginalisation) (q_μ::PointMass, q_W::PointMass, q_λ::PointMass, q_ν::PointMass) = MvNormalWishart(mean(q_μ), mean(q_W), mean(q_λ), mean(q_ν))
1 change: 1 addition & 0 deletions src/rules/predefined.jl
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Expand Up @@ -55,6 +55,7 @@ include("mv_normal_mean_covariance/out.jl")
include("mv_normal_mean_covariance/mean.jl")
include("mv_normal_mean_covariance/covariance.jl")
include("mv_normal_mean_covariance/marginals.jl")
include("mv_normal_wishart/out.jl")

include("normal_mean_variance/out.jl")
include("normal_mean_variance/mean.jl")
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14 changes: 14 additions & 0 deletions test/rules/mv_normal_wishart/out_tests.jl
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@testitem "rules:MvNormalWishart:out" begin
using ReactiveMP, BayesBase, Random, ExponentialFamily, Distributions

import ReactiveMP: @test_rules

@testset "Variational Message Passing: (q_μ::PointMass, q_λ::PointMass, q_W::PointMass, q_ν::PointMass)" begin
# Type promotion is false because λ and ν will also be promoted but not necessarily promote μ and W.
@test_rules [check_type_promotion = false] MvNormalWishart(:out, Marginalisation) [(
input = (q_μ = PointMass([1.0, 2.0]), q_W = PointMass([1.0 0.0; 0.0 1.0]), q_λ = PointMass(1.0), q_ν = PointMass(1.0)),
output = MvNormalWishart([1.0, 2.0], [1.0 0.0; 0.0 1.0], 1.0, 1.0)
),]
end
end # testset
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