draft MvNormal

Project.toml

@@ -18,7 +18,6 @@ MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
 NamedTupleTools = "d9ec5142-1e00-5aa0-9d6a-321866360f50"
 NestedTuples = "a734d2a7-8d68-409b-9419-626914d4061d"
-PositiveFactorizations = "85a6dd25-e78a-55b7-8502-1745935b8125"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
@@ -39,7 +38,6 @@ MacroTools = "0.5"
 MappedArrays = "0.3, 0.4"
 NamedTupleTools = "0.13"
 NestedTuples = "0.3"
-PositiveFactorizations = "0.2"
 SpecialFunctions = "0.10, 1"
 StatsFuns = "0.9"
 TransformVariables = "0.4"

src/MeasureTheory.jl

@@ -91,11 +91,11 @@ include("parameterized/bernoulli.jl")
 include("parameterized/poisson.jl")
 include("parameterized/binomial.jl")
 include("parameterized/multinomial.jl")
-include("parameterized/lkj-cholesky.jl")
+# include("parameterized/lkj-cholesky.jl")
 include("parameterized/negativebinomial.jl")
 
-include("transforms/corrcholesky.jl")
-include("transforms/ordered.jl")
+# include("transforms/corrcholesky.jl")
+# include("transforms/ordered.jl")
 
 include("density.jl")
 # include("pushforward.jl")

src/parameterized/mvnormal.jl

@@ -8,55 +8,18 @@ using Random
 import Base
 
 
-struct MvNormal{N, T, I, J} <: ParameterizedMeasure{N}
-    par::NamedTuple{N, T}
-end
+@parameterized MvNormal(μ, Σ)
 
+@kwstruct MvNormal(Σ)
 
-function MvNormal(nt::NamedTuple{N,T}) where {N,T}
-    I,J = mvnormaldims(nt)
+@kwstruct MvNormal(μ, Σ)
 
-    cache = Vector{Float64}(undef, max(I,J))
-    MvNormal{N,T,I,J}(cache, nt)
-end
+@kwstruct MvNormal(σ)
 
-function Base.size(d::MvNormal{N, T, I, J}) where {N,T,I,J}
-    return (I,)
-end
+@kwstruct MvNormal(μ, σ)
 
-mvnormaldims(nt::NamedTuple{(:A, :b)}) = size(nt.A)
+@kwstruct MvNormal(μ, Ω)
 
-function MeasureTheory.basemeasure(μ::MvNormal{N, T, I,I}) where {N, T, I}
-    return (1 / sqrt2π)^I * Lebesgue(ℝ)^I
-end
+@kwstruct MvNormal(Ω)
 
-sampletype(d::MvNormal{N, T, I, J}) where {N,T,I,J} = Vector{Float64}
-
-MvNormal(; kwargs...) = begin
-    MvNormal((; kwargs...))
-end
-
-
-
-function Random.rand!(rng::AbstractRNG, d::MvNormal{(:A, :b),T,I,J}, x::AbstractArray) where {T,I,J}
-    z = getcache(d, J)
-    rand!(rng, Normal()^J, z)
-    copyto!(x, d.b)
-    mul!(x, d.A, z, 1.0, 1.0)
-    return x
-end
-
-function logdensity(d::MvNormal{(:A,:b)}, x)
-    cache = getcache(d, size(d))
-    z = d.A \ (x - d.b)
-    return logdensity(MvNormal(), z) - logabsdet(d.A)
-end
-
-≪(::MvNormal, ::Lebesgue{ℝ}) = true
-
-# function logdensity(d::MvNormal{(:Σ⁻¹,)}, x)
-#     @tullio ℓ = -0.5 * x[i] * d.Σ⁻¹[i,j] * x[j]
-#     return ℓ
-# end
-
-mvnormaldims(nt::NamedTuple{(:Σ⁻¹,)}) = size(nt.Σ⁻¹)
+@kwstruct MvNormal(ω)