Add MvNormalMeanScalePrecision distribution

Project.toml

@@ -28,7 +28,7 @@ TinyHugeNumbers = "783c9a47-75a3-44ac-a16b-f1ab7b3acf04"
 
 [compat]
 Aqua = "0.8.7"
-BayesBase = "1.2"
+BayesBase = "1.5.0"
 Distributions = "0.25"
 DomainSets = "0.5.2, 0.6, 0.7"
 FastCholesky = "1.0"
@@ -57,8 +57,8 @@ Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 CpuId = "adafc99b-e345-5852-983c-f28acb93d879"
 JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b"
-RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 ReTestItems = "817f1d60-ba6b-4fd5-9520-3cf149f6a823"
+RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 

docs/src/library.md

@@ -14,6 +14,7 @@ ExponentialFamily.NormalWeightedMeanPrecision
 ExponentialFamily.MvNormalMeanPrecision
 ExponentialFamily.MvNormalMeanCovariance
 ExponentialFamily.MvNormalWeightedMeanPrecision
+ExponentialFamily.MvNormalMeanScalePrecision
 ExponentialFamily.JointNormal
 ExponentialFamily.JointGaussian
 ExponentialFamily.WishartFast

src/ExponentialFamily.jl

@@ -46,6 +46,7 @@ include("distributions/normal_family/mv_normal_mean_covariance.jl")
 include("distributions/normal_family/mv_normal_mean_precision.jl")
 include("distributions/normal_family/mv_normal_weighted_mean_precision.jl")
 include("distributions/normal_family/normal_family.jl")
+include("distributions/normal_family/mv_normal_mean_scale_precision.jl")
 include("distributions/gamma_inverse.jl")
 include("distributions/geometric.jl")
 include("distributions/matrix_dirichlet.jl")

src/distributions/normal_family/mv_normal_mean_scale_precision.jl

@@ -0,0 +1,268 @@
+export MvNormalMeanScalePrecision, MvGaussianMeanScalePrecision
+
+import Distributions: logdetcov, distrname, sqmahal, sqmahal!, AbstractMvNormal
+import LinearAlgebra: diag, Diagonal, dot
+import Base: ndims, precision, length, size, prod
+
+"""
+    MvNormalMeanScalePrecision{T <: Real, M <: AbstractVector{T}} <: AbstractMvNormal
+
+A multivariate normal distribution with mean `μ` and scale parameter `γ` that scales the identity precision matrix.
+
+# Type Parameters
+- `T`: The element type of the mean vector and scale parameter
+- `M`: The type of the mean vector, which must be a subtype of `AbstractVector{T}`
+
+# Fields
+- `μ::M`: The mean vector of the multivariate normal distribution
+- `γ::T`: The scale parameter that scales the identity precision matrix
+
+# Notes
+The precision matrix of this distribution is `γ * I`, where `I` is the identity matrix.
+The covariance matrix is the inverse of the precision matrix, i.e., `(1/γ) * I`.
+"""
+struct MvNormalMeanScalePrecision{T <: Real, M <: AbstractVector{T}} <: AbstractMvNormal
+    μ::M
+    γ::T
+end
+
+const MvGaussianMeanScalePrecision = MvNormalMeanScalePrecision
+
+function MvNormalMeanScalePrecision(μ::AbstractVector{<:Real}, γ::Real)
+    T = promote_type(eltype(μ), eltype(γ))
+    return MvNormalMeanScalePrecision(convert(AbstractArray{T}, μ), convert(T, γ))
+end
+
+function MvNormalMeanScalePrecision(μ::AbstractVector{<:Integer}, γ::Real)
+    return MvNormalMeanScalePrecision(float.(μ), float(γ))
+end
+
+function MvNormalMeanScalePrecision(μ::AbstractVector{T}) where {T}
+    return MvNormalMeanScalePrecision(μ, convert(T, 1))
+end
+
+function MvNormalMeanScalePrecision(μ::AbstractVector{T1}, γ::T2) where {T1, T2}
+    T = promote_type(T1, T2)
+    μ_new = convert(AbstractArray{T}, μ)
+    γ_new = convert(T, γ)(length(μ))
+    return MvNormalMeanScalePrecision(μ_new, γ_new)
+end
+
+function unpack_parameters(::Type{MvNormalMeanScalePrecision}, packed)
+    p₁ = view(packed, 1:length(packed)-1)
+    p₂ = packed[end]
+
+    return (p₁, p₂)
+end
+
+function isproper(::NaturalParametersSpace, ::Type{MvNormalMeanScalePrecision}, η, conditioner)
+    k = length(η) - 1
+    if length(η) < 2 || (length(η) !== k + 1)
+        return false
+    end
+    (η₁, η₂) = unpack_parameters(MvNormalMeanScalePrecision, η)
+    return isnothing(conditioner) && isone(size(η₂, 1)) && isposdef(-η₂)
+end
+
+function (::MeanToNatural{MvNormalMeanScalePrecision})(tuple_of_θ::Tuple{Any, Any})
+    (μ, γ) = tuple_of_θ
+    return (γ * μ, - γ / 2)
+end
+
+function (::NaturalToMean{MvNormalMeanScalePrecision})(tuple_of_η::Tuple{Any, Any})
+    (η₁, η₂) = tuple_of_η
+    γ = -2 * η₂
+    return (η₁ / γ, γ)
+end
+
+function nabs2(x)
+    return sum(map(abs2, x))
+end
+
+getsufficientstatistics(::Type{MvNormalMeanScalePrecision}) = (identity, nabs2)
+
+# Conversions
+function Base.convert(
+    ::Type{MvNormal{T, C, M}},
+    dist::MvNormalMeanScalePrecision
+) where {T <: Real, C <: Distributions.PDMats.PDMat{T, Matrix{T}}, M <: AbstractVector{T}}
+    m, σ = mean(dist), std(dist)
+    return MvNormal(convert(M, m), convert(T, σ))
+end
+
+function Base.convert(
+    ::Type{MvNormalMeanScalePrecision{T, M}},
+    dist::MvNormalMeanScalePrecision
+) where {T <: Real, M <: AbstractArray{T}}
+    m, γ = mean(dist), dist.γ
+    return MvNormalMeanScalePrecision{T, M}(convert(M, m), convert(T, γ))
+end
+
+function Base.convert(
+    ::Type{MvNormalMeanScalePrecision{T}},
+    dist::MvNormalMeanScalePrecision
+) where {T <: Real}
+    return convert(MvNormalMeanScalePrecision{T, AbstractArray{T, 1}}, dist)
+end
+
+function Base.convert(::Type{MvNormalMeanCovariance}, dist::MvNormalMeanScalePrecision)
+    m, σ = mean(dist), cov(dist)
+    return MvNormalMeanCovariance(m, σ * diagm(ones(length(m))))
+end
+
+function Base.convert(::Type{MvNormalMeanPrecision}, dist::MvNormalMeanScalePrecision)
+    m, γ = mean(dist), precision(dist)
+    return MvNormalMeanPrecision(m, γ * diagm(ones(length(m))))
+end
+
+function Base.convert(::Type{MvNormalWeightedMeanPrecision}, dist::MvNormalMeanScalePrecision)
+    m, γ = mean(dist), precision(dist)
+    return MvNormalWeightedMeanPrecision(γ * m, γ * diagm(ones(length(m))))
+end
+
+Distributions.distrname(::MvNormalMeanScalePrecision) = "MvNormalMeanScalePrecision"
+
+BayesBase.weightedmean(dist::MvNormalMeanScalePrecision) = precision(dist) * mean(dist)
+
+BayesBase.mean(dist::MvNormalMeanScalePrecision)      = dist.μ
+BayesBase.mode(dist::MvNormalMeanScalePrecision)      = mean(dist)
+BayesBase.var(dist::MvNormalMeanScalePrecision)       = diag(cov(dist))
+BayesBase.cov(dist::MvNormalMeanScalePrecision)       = cholinv(invcov(dist))
+BayesBase.invcov(dist::MvNormalMeanScalePrecision)    = scale(dist) * I(length(mean(dist)))
+BayesBase.std(dist::MvNormalMeanScalePrecision)       = cholsqrt(cov(dist))
+BayesBase.logdetcov(dist::MvNormalMeanScalePrecision) = -chollogdet(invcov(dist))
+BayesBase.scale(dist::MvNormalMeanScalePrecision)     = dist.γ
+BayesBase.params(dist::MvNormalMeanScalePrecision)    = (mean(dist), scale(dist))
+
+function Distributions.sqmahal(dist::MvNormalMeanScalePrecision, x::AbstractVector)
+    T = promote_type(eltype(x), paramfloattype(dist))
+    return sqmahal!(similar(x, T), dist, x)
+end
+
+function Distributions.sqmahal!(r, dist::MvNormalMeanScalePrecision, x::AbstractVector)
+    μ, γ = params(dist)
+    @inbounds @simd for i in 1:length(r)
+        r[i] = μ[i] - x[i]
+    end
+    return dot3arg(r, γ, r) # x' * A * x
+end
+
+Base.eltype(::MvNormalMeanScalePrecision{T}) where {T} = T
+Base.precision(dist::MvNormalMeanScalePrecision) = invcov(dist)
+Base.length(dist::MvNormalMeanScalePrecision) = length(mean(dist))
+Base.ndims(dist::MvNormalMeanScalePrecision) = length(dist)
+Base.size(dist::MvNormalMeanScalePrecision) = (length(dist),)
+
+Base.convert(::Type{<:MvNormalMeanScalePrecision}, μ::AbstractVector, γ::Real) = MvNormalMeanScalePrecision(μ, γ)
+
+function Base.convert(::Type{<:MvNormalMeanScalePrecision{T}}, μ::AbstractVector, γ::Real) where {T <: Real}
+    MvNormalMeanScalePrecision(convert(AbstractArray{T}, μ), convert(T, γ))
+end
+
+BayesBase.vague(::Type{<:MvNormalMeanScalePrecision}, dims::Int) =
+    MvNormalMeanScalePrecision(zeros(Float64, dims), convert(Float64, tiny))
+
+BayesBase.default_prod_rule(::Type{<:MvNormalMeanScalePrecision}, ::Type{<:MvNormalMeanScalePrecision}) = PreserveTypeProd(Distribution)
+
+function BayesBase.prod(::PreserveTypeProd{Distribution}, left::MvNormalMeanScalePrecision, right::MvNormalMeanScalePrecision)
+    w = scale(left) + scale(right)
+    m = (scale(left) * mean(left) + scale(right) * mean(right)) / w
+    return MvNormalMeanScalePrecision(m, w)
+end
+
+BayesBase.default_prod_rule(::Type{<:MultivariateNormalDistributionsFamily}, ::Type{<:MvNormalMeanScalePrecision}) = PreserveTypeProd(Distribution)
+
+function BayesBase.prod(
+    ::PreserveTypeProd{Distribution},
+    left::L,
+    right::R
+) where {L <: MultivariateNormalDistributionsFamily, R <: MvNormalMeanScalePrecision}
+    wleft  = convert(MvNormalWeightedMeanPrecision, left)
+    wright = convert(MvNormalWeightedMeanPrecision, right)
+    return prod(BayesBase.default_prod_rule(wleft, wright), wleft, wright)
+end
+
+function BayesBase.rand(rng::AbstractRNG, dist::MvGaussianMeanScalePrecision{T}) where {T}
+    μ, γ = params(dist)
+    d = length(μ)
+    return rand!(rng, dist, Vector{T}(undef, d))
+end
+
+function BayesBase.rand(rng::AbstractRNG, dist::MvGaussianMeanScalePrecision{T}, size::Int64) where {T}
+    container = Matrix{T}(undef, length(dist), size)
+    return rand!(rng, dist, container)
+end
+
+# FIXME: This is not the most efficient way to generate random samples within container
+#        it needs to work with scale method, not with std
+function BayesBase.rand!(
+    rng::AbstractRNG,
+    dist::MvGaussianMeanScalePrecision,
+    container::AbstractArray{T}
+) where {T <: Real}
+    preallocated = similar(container)
+    randn!(rng, reshape(preallocated, length(preallocated)))
+    μ, L = mean_std(dist)
+    @views for i in axes(preallocated, 2)
+        copyto!(container[:, i], μ)
+        mul!(container[:, i], L, preallocated[:, i], 1, 1)
+    end
+    container
+end
+
+function getsupport(ef::ExponentialFamilyDistribution{MvNormalMeanScalePrecision})
+    dim = length(getnaturalparameters(ef)) - 1
+    return Domain(IndicatorFunction{AbstractVector}(MvNormalDomainIndicator(dim)))
+end
+
+isbasemeasureconstant(::Type{MvNormalMeanScalePrecision}) = ConstantBaseMeasure()
+
+getbasemeasure(::Type{MvNormalMeanScalePrecision}) = (x) -> (2π)^(-length(x) / 2)
+
+getlogbasemeasure(::Type{MvNormalMeanScalePrecision}) = (x) -> -length(x) / 2 * log2π
+
+getlogpartition(::NaturalParametersSpace, ::Type{MvNormalMeanScalePrecision}) =
+    (η) -> begin
+        η1 = @view η[1:end-1]
+        η2 = η[end]
+        k = length(η1)
+        Cinv = inv(η2)
+        return -dot(η1, 1/4*Cinv, η1) - (k / 2)*log(-2*η2)
+    end
+
+getgradlogpartition(::NaturalParametersSpace, ::Type{MvNormalMeanScalePrecision}) = 
+    (η) -> begin
+        η1 = @view η[1:end-1]
+        η2 = η[end]
+        inv2 = inv(η2)
+        k = length(η1)
+        return pack_parameters(MvNormalMeanCovariance, (-1/(2*η2) * η1,  dot(η1,η1) / 4*inv2^2 - k/2 * inv2))
+    end
+
+getfisherinformation(::NaturalParametersSpace, ::Type{MvNormalMeanScalePrecision}) = 
+    (η) -> begin
+        η1 = @view η[1:end-1]
+        η2 = η[end]
+        k = length(η1)
+
+        η1_part = -inv(2*η2)* I(length(η1))
+        η1η2 = zeros(k, 1)
+        η1η2 .= η1*inv(2*η2^2)
+
+        η2_part = k*inv(2abs2(η2)) - dot(η1,η1) / (2*η2^3)
+
+        return ArrowheadMatrix(η2_part, η1η2, diag(η1_part))
+    end
+
+
+getfisherinformation(::MeanParametersSpace, ::Type{MvNormalMeanScalePrecision}) = 
+    (θ) -> begin
+        μ = @view θ[1:end-1]
+        γ = θ[end]
+        k = length(μ)
+
+        matrix = zeros(eltype(μ), (k+1))
+        matrix[1:k] .= γ
+        matrix[k+1] = k*inv(2abs2(γ))
+        return Diagonal(matrix)
+    end

test/distributions/distributions_setuptests.jl

@@ -557,4 +557,4 @@ function test_generic_simple_exponentialfamily_product(
     end
 
     return true
-end
+end