Add sufficient statistics and MLE for Chi and Chisq

Project.toml

@@ -1,7 +1,7 @@
 name = "Distributions"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
 authors = ["JuliaStats"]
-version = "0.25.120"
+version = "0.25.121"
 
 [deps]
 AliasTables = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"

docs/src/fit.md

@@ -43,6 +43,8 @@ The `fit_mle` method has been implemented for the following distributions:
 - [`Beta`](@ref)
 - [`Binomial`](@ref)
 - [`Categorical`](@ref)
+- [`Chi`](@ref)
+- [`Chisq`](@ref)
 - [`DiscreteUniform`](@ref)
 - [`Exponential`](@ref)
 - [`LogNormal`](@ref)

src/univariate/continuous/chi.jl

@@ -23,7 +23,7 @@ External links
 """
 struct Chi{T<:Real} <: ContinuousUnivariateDistribution
     ν::T
-    Chi{T}(ν::T) where {T} = new{T}(ν)
+    Chi{T}(ν::Real) where {T<:Real} = new{T}(ν)
 end
 
 function Chi(ν::Real; check_args::Bool=true)
@@ -119,3 +119,22 @@ end
 rand(rng::AbstractRNG, s::ChiSampler) = sqrt(rand(rng, s.s))
 
 sampler(d::Chi) = ChiSampler(sampler(Chisq(d.ν)))
+
+
+#### Fitting
+
+struct ChiStats{T<:Real} <: SufficientStats
+    # (Weighted) mean of log(x)
+    mlogx::T
+end
+
+suffstats(::Type{<:Chi}, x::AbstractArray{<:Real}) = ChiStats(mean(log, x))
+function suffstats(::Type{<:Chi}, x::AbstractArray{<:Real}, w::AbstractArray{<:Real})
+    if axes(x) != axes(w)
+        throw(DimensionMismatch("Inconsistent array dimensions: Axes of samples and sample weights must be equal."))
+    end
+    mlogx = sum(Broadcast.instantiate(Broadcast.broadcasted(xlogy, w, x))) / sum(w)
+    return ChiStats(mlogx)
+end
+
+fit_mle(::Type{T}, ss::ChiStats) where {T<:Chi} = T(2 * invdigamma(2 * ss.mlogx - logtwo))

src/univariate/continuous/chisq.jl

@@ -22,7 +22,7 @@ External links
 """
 struct Chisq{T<:Real} <: ContinuousUnivariateDistribution
     ν::T
-    Chisq{T}(ν::T) where {T} = new{T}(ν)
+    Chisq{T}(ν::Real) where {T<:Real} = new{T}(ν)
 end
 
 function Chisq(ν::Real; check_args::Bool=true)
@@ -107,3 +107,21 @@ function sampler(d::Chisq)
     θ = oftype(α, 2)
     return sampler(Gamma{typeof(α)}(α, θ))
 end
+
+#### Fitting
+
+struct ChisqStats{T<:Real} <: SufficientStats
+    # (Weighted) mean of log(x)
+    mlogx::T
+end
+
+suffstats(::Type{<:Chisq}, x::AbstractArray{<:Real}) = ChisqStats(mean(log, x))
+function suffstats(::Type{<:Chisq}, x::AbstractArray{<:Real}, w::AbstractArray{<:Real})
+    if axes(x) != axes(w)
+        throw(DimensionMismatch("Inconsistent array dimensions: Axes of samples and sample weights must be equal."))
+    end
+    mlogx = sum(Broadcast.instantiate(Broadcast.broadcasted(xlogy, w, x))) / sum(w)
+    return ChisqStats(mlogx)
+end
+
+fit_mle(::Type{T}, ss::ChisqStats) where {T<:Chisq} = T(2 * invdigamma(ss.mlogx - logtwo))

test/fit.jl

@@ -6,6 +6,7 @@
 
 using Distributions
 using OffsetArrays
+using ForwardDiff
 using Test, Random, LinearAlgebra
 
 
@@ -465,3 +466,64 @@ end
 
     end
 end
+
+@testset "Testing fit for Chi" begin
+    ν = 3.1
+    for func in funcs, D in (Chi, Chi{Float64}, Chi{Float32})
+        v = func[1](n0)
+        z = func[2](D(ν), n0)
+        for x in (z, OffsetArray(z, -n0 ÷ 2)), w in (v, OffsetArray(v, -n0 ÷ 2))
+            ss = @inferred suffstats(D, x)
+            @test ss isa Distributions.ChiStats
+            @test ss.mlogx ≈ mean(log.(x))
+
+            d = @inferred fit(D, x)
+            @test d isa D
+            @test ForwardDiff.derivative(ν -> sum(logpdf.(Chi(ν), x)), dof(d)) ≈ 0 atol = (eps(partype(d)))^(2/3)
+
+            if axes(x) == axes(w)
+                d = @inferred fit(D, x, w)
+                @test d isa D
+                @test ForwardDiff.derivative(ν -> dot(logpdf.(Chi(ν), x), w), dof(d)) ≈ 0 atol = (eps(partype(d)))^(2/3)
+
+                ss = @inferred suffstats(D, x, w)
+                @test ss isa Distributions.ChiStats
+                @test ss.mlogx ≈ dot(w ./ sum(w), log.(x))
+            else
+                @test_throws DimensionMismatch("Inconsistent array dimensions: Axes of samples and sample weights must be equal.") suffstats(D, x, w)
+                @test_throws DimensionMismatch("Inconsistent array dimensions: Axes of samples and sample weights must be equal.") fit(D, x, w)
+            end
+        end
+    end
+end
+
+
+@testset "Testing fit for Chisq" begin
+    ν = 4.3
+    for func in funcs, D in (Chisq, Chisq{Float64}, Chisq{Float32})
+        v = func[1](n0)
+        z = func[2](D(ν), n0)
+        for x in (z, OffsetArray(z, -n0 ÷ 2)), w in (v, OffsetArray(v, -n0 ÷ 2))
+            ss = @inferred suffstats(D, x)
+            @test ss isa Distributions.ChisqStats
+            @test ss.mlogx ≈ mean(log.(x))
+
+            d = @inferred fit(D, x)
+            @test d isa D
+            @test ForwardDiff.derivative(ν -> sum(logpdf.(Chisq(ν), x)), dof(d)) ≈ 0 atol = (eps(partype(d)))^(2/3)
+
+            if axes(x) == axes(w)
+                ss = @inferred suffstats(D, x, w)
+                @test ss isa Distributions.ChisqStats
+                @test ss.mlogx ≈ dot(w ./ sum(w), log.(x))
+
+                d = @inferred fit(D, x, w)
+                @test d isa D
+                @test ForwardDiff.derivative(ν -> dot(logpdf.(Chisq(ν), x), w), dof(d)) ≈ 0 atol = (eps(partype(d)))^(2/3)
+            else
+                @test_throws DimensionMismatch("Inconsistent array dimensions: Axes of samples and sample weights must be equal.") suffstats(D, x, w)
+                @test_throws DimensionMismatch("Inconsistent array dimensions: Axes of samples and sample weights must be equal.") fit(D, x, w)
+            end
+        end
+    end
+end