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Make suffstats and fit_mle type-generic for Normal{T} #1560

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Make suffstats and fit_mle type-generic for Normal{T} #1560

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debbf4f
Make suffstats and fit_mle type generic for Normal{T}
gdalle May 30, 2022
7443d7b
Merge branch 'JuliaStats:master' into type_generic_normal
gdalle Jun 9, 2023
4b27b30
Type generic partially known Normal
gdalle Jun 9, 2023
27ea06f
Merge branch 'master' into type_generic_normal
gdalle Jan 13, 2024
de449b2
Fix known stats and minimize diff
gdalle Jan 13, 2024
9203e63
Useless diff
gdalle Jan 13, 2024
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32 changes: 16 additions & 16 deletions src/univariate/continuous/normal.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -105,14 +105,14 @@ rand(rng::AbstractRNG, d::Normal{T}) where {T} = d.μ + d.σ * randn(rng, float(

#### Fitting

struct NormalStats <: SufficientStats
s::Float64 # (weighted) sum of x
m::Float64 # (weighted) mean of x
s2::Float64 # (weighted) sum of (x - μ)^2
tw::Float64 # total sample weight
struct NormalStats{T} <: SufficientStats
s::T # (weighted) sum of x
m::T # (weighted) mean of x
s2::T # (weighted) sum of (x - μ)^2
tw::T # total sample weight
end
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function suffstats(::Type{<:Normal}, x::AbstractArray{T}) where T<:Real
function suffstats(::Type{Normal{T}}, x::AbstractArray{<:Real}) where {T<:Real}
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n = length(x)

# compute s
Expand All @@ -128,10 +128,10 @@ function suffstats(::Type{<:Normal}, x::AbstractArray{T}) where T<:Real
@inbounds s2 += abs2(x[i] - m)
end

NormalStats(s, m, s2, n)
NormalStats{T}(s, m, s2, n)
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end

function suffstats(::Type{<:Normal}, x::AbstractArray{T}, w::AbstractArray{Float64}) where T<:Real
function suffstats(::Type{Normal{T}}, x::AbstractArray{<:Real}, w::AbstractArray{<:Real}) where {T<:Real}
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n = length(x)

# compute s
Expand All @@ -150,7 +150,7 @@ function suffstats(::Type{<:Normal}, x::AbstractArray{T}, w::AbstractArray{Float
@inbounds s2 += w[i] * abs2(x[i] - m)
end

NormalStats(s, m, s2, tw)
NormalStats{T}(s, m, s2, tw)
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end

# Cases where μ or σ is known
Expand Down Expand Up @@ -211,16 +211,16 @@ end

# fit_mle based on sufficient statistics

fit_mle(::Type{<:Normal}, ss::NormalStats) = Normal(ss.m, sqrt(ss.s2 / ss.tw))
fit_mle(::Type{D}, ss::NormalStats) where {D<:Normal} = D(ss.m, sqrt(ss.s2 / ss.tw))
fit_mle(g::NormalKnownMu, ss::NormalKnownMuStats) = Normal(g.μ, sqrt(ss.s2 / ss.tw))
fit_mle(g::NormalKnownSigma, ss::NormalKnownSigmaStats) = Normal(ss.sx / ss.tw, g.σ)
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# generic fit_mle methods

function fit_mle(::Type{<:Normal}, x::AbstractArray{T}; mu::Float64=NaN, sigma::Float64=NaN) where T<:Real
function fit_mle(::Type{D}, x::AbstractArray{<:Real}; mu::Float64=NaN, sigma::Float64=NaN) where {D<:Normal}
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if isnan(mu)
if isnan(sigma)
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fit_mle(Normal, suffstats(Normal, x))
fit_mle(D, suffstats(Normal, x))
else
g = NormalKnownSigma(sigma)
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We should find a way to preserve/forward the type of D in these cases as well.

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Which eltype should we pick for D though, the one of g or the one of the suffstats?

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I added a conversion in my latest commit, what do you think?

fit_mle(g, suffstats(g, x))
Expand All @@ -230,15 +230,15 @@ function fit_mle(::Type{<:Normal}, x::AbstractArray{T}; mu::Float64=NaN, sigma::
g = NormalKnownMu(mu)
fit_mle(g, suffstats(g, x))
else
Normal(mu, sigma)
D(mu, sigma)
end
end
end

function fit_mle(::Type{<:Normal}, x::AbstractArray{T}, w::AbstractArray{Float64}; mu::Float64=NaN, sigma::Float64=NaN) where T<:Real
function fit_mle(::Type{D}, x::AbstractArray{<:Real}, w::AbstractArray{<:Real}; mu::Float64=NaN, sigma::Float64=NaN) where {D<:Normal}
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if isnan(mu)
if isnan(sigma)
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fit_mle(Normal, suffstats(Normal, x, w))
fit_mle(D, suffstats(Normal, x, w))
else
g = NormalKnownSigma(sigma)
fit_mle(g, suffstats(g, x, w))
Expand All @@ -248,7 +248,7 @@ function fit_mle(::Type{<:Normal}, x::AbstractArray{T}, w::AbstractArray{Float64
g = NormalKnownMu(mu)
fit_mle(g, suffstats(g, x, w))
else
Normal(mu, sigma)
D(mu, sigma)
end
end
end