Add mean et al. for truncated log normal

src/truncate.jl

@@ -261,6 +261,7 @@ include(joinpath("truncated", "exponential.jl"))
 include(joinpath("truncated", "uniform.jl"))
 include(joinpath("truncated", "loguniform.jl"))
 include(joinpath("truncated", "discrete_uniform.jl"))
+include(joinpath("truncated", "lognormal.jl"))
 
 #### Utilities
 

src/truncated/lognormal.jl

@@ -0,0 +1,50 @@
+# Moments of the truncated log-normal can be computed directly from the moment generating
+# function of the truncated normal:
+#   Let Y ~ LogNormal(μ, σ) truncated to (a, b). Then log(Y) ~ Normal(μ, σ) truncated
+#   to (log(a), log(b)), and E[Y^n] = E[(e^log(Y))^n] = E[e^(nlog(Y))] = mgf(log(Y), n).
+
+# Given `truncate(LogNormal(μ, σ), a, b)`, return `truncate(Normal(μ, σ), log(a), log(b))`
+function _truncnorm(d::Truncated{<:LogNormal})
+    μ, σ = params(d.untruncated)
+    T = float(partype(d))
+    a = d.lower === nothing || d.lower <= 0 ? nothing : log(T(d.lower))
+    b = d.upper === nothing || isinf(d.upper) ? nothing : log(T(d.upper))
+    return truncated(Normal{T}(T(μ), T(σ)), a, b)
+end
+
+mean(d::Truncated{<:LogNormal}) = mgf(_truncnorm(d), 1)
+
+function var(d::Truncated{<:LogNormal})
+    tn = _truncnorm(d)
+    # Ensure the variance doesn't end up negative, which can occur due to numerical issues
+    return max(mgf(tn, 2) - mgf(tn, 1)^2, 0)
+end
+
+function skewness(d::Truncated{<:LogNormal})
+    tn = _truncnorm(d)
+    m1 = mgf(tn, 1)
+    m2 = mgf(tn, 2)
+    m3 = mgf(tn, 3)
+    sqm1 = m1^2
+    v = m2 - sqm1
+    return (m3 + m1 * (-3 * m2 + 2 * sqm1)) / (v * sqrt(v))
+end
+
+function kurtosis(d::Truncated{<:LogNormal})
+    tn = _truncnorm(d)
+    m1 = mgf(tn, 1)
+    m2 = mgf(tn, 2)
+    m3 = mgf(tn, 3)
+    m4 = mgf(tn, 4)
+    v = m2 - m1^2
+    return @horner(m1, m4, -4m3, 6m2, 0, -3) / v^2 - 3
+end
+
+# TODO: The entropy can be written "directly" as well, according to Mathematica, but
+# the expression for it fills me with regret. There are some recognizable components,
+# so a sufficiently motivated person could try to manually simplify it into something
+# comprehensible. For reference, you can obtain the entropy with Mathematica like so:
+#
+#   d = TruncatedDistribution[{a, b}, LogNormalDistribution[m, s]];
+#   Expectation[-LogLikelihood[d, {x}], Distributed[x, d],
+#               Assumptions -> Element[x | m | s | a | b, Reals] && s > 0 && 0 < a < x < b]

src/truncated/normal.jl

@@ -118,6 +118,36 @@ function entropy(d::Truncated{<:Normal{<:Real},Continuous})
     0.5 * (log2π + 1.) + log(σ * z) + (aφa - bφb) / (2.0 * z)
 end
 
+function mgf(d::Truncated{<:Normal{<:Real},Continuous}, t::Real)
+    T = float(promote_type(partype(d), typeof(t)))
+    a = T(minimum(d))
+    b = T(maximum(d))
+    if isnan(a) || isnan(b)  # TODO: Disallow constructing `Truncated` with a `NaN` bound?
+        return T(NaN)
+    elseif isinf(a) && isinf(b) && a != b
+        # Distribution is `Truncated`-wrapped but not actually truncated
+        return T(mgf(d.untruncated, t))
+    elseif a == b
+        # Truncated to a Dirac distribution; this is `mgf(Dirac(a), t)`
+        return exp(a * t)
+    end
+    d0 = d.untruncated
+    μ = mean(d0)
+    σ = std(d0)
+    σ²t = σ^2 * t
+    a′ = (a - μ) / σ
+    b′ = (b - μ) / σ
+    stdnorm = Normal{T}(zero(T), one(T))
+    # log((Φ(b′ - σ²t) - Φ(a′ - σ²t)) / (Φ(b′) - Φ(a′)))
+    logratio = if isfinite(a) && isfinite(b)  # doubly truncated
+        logdiffcdf(stdnorm, b′ - σ²t, a′ - σ²t) - logdiffcdf(stdnorm, b′, a′)
+    elseif isfinite(a)  # left truncated: b = ∞, Φ(b′) = Φ(b′ - σ²t) = 1
+        logccdf(stdnorm, a′ - σ²t) - logccdf(stdnorm, a′)
+    else  # isfinite(b), right truncated: a = ∞, Φ(a′) = Φ(a′ - σ²t) = 0
+        logcdf(stdnorm, b′ - σ²t) - logcdf(stdnorm, b′)
+    end
+    return exp(t * (μ + σ²t / 2) + logratio)
+end
 
 ### sampling
 

test/runtests.jl

@@ -20,6 +20,7 @@ const tests = [
     "truncated/exponential",
     "truncated/uniform",
     "truncated/discrete_uniform",
+    "truncated/lognormal",
     "censored",
     "univariate/continuous/normal",
     "univariate/continuous/laplace",

test/testutils.jl

@@ -18,6 +18,17 @@ function _linspace(a::Float64, b::Float64, n::Int)
     return r
 end
 
+# Enables testing against values computed at high precision by transforming an expression
+# that uses numeric literals and constants to wrap those in `big()`, similar to how the
+# high-precision values for irrational constants are defined with `Base.@irrational` and
+# in IrrationalConstants.jl. See e.g. `test/truncated/normal.jl` for example use.
+bigly(x) = x
+bigly(x::Symbol) = x in (:π, :ℯ, :Inf, :NaN) ? Expr(:call, :big, x) : x
+bigly(x::Real) = Expr(:call, :big, x)
+bigly(x::Expr) = (map!(bigly, x.args, x.args); x)
+macro bigly(ex)
+    return esc(bigly(ex))
+end
 
 #################################################
 #

test/truncated/lognormal.jl

@@ -0,0 +1,40 @@
+using Distributions, Test
+using Distributions: expectation
+
+naive_moment(d, n, μ, σ²) = (σ = sqrt(σ²); expectation(x -> ((x - μ) / σ)^n, d))
+
+@testset "Truncated log normal" begin
+    @testset "truncated(LogNormal{$T}(0, 1), ℯ⁻², ℯ²)" for T in (Float32, Float64, BigFloat)
+        d = truncated(LogNormal{T}(zero(T), one(T)), exp(T(-2)), exp(T(2)))
+        tn = truncated(Normal{BigFloat}(big(0.0), big(1.0)), -2, 2)
+        bigmean = mgf(tn, 1)
+        bigvar = mgf(tn, 2) - bigmean^2
+        @test @inferred(mean(d)) ≈ bigmean
+        @test @inferred(var(d)) ≈ bigvar
+        @test @inferred(median(d)) ≈ one(T)
+        @test @inferred(skewness(d)) ≈ naive_moment(d, 3, bigmean, bigvar)
+        @test @inferred(kurtosis(d)) ≈ naive_moment(d, 4, bigmean, bigvar) - big(3)
+        @test mean(d) isa T
+    end
+    @testset "Bound with no effect" begin
+        # Uses the example distribution from issue #709, though what's tested here is
+        # mostly unrelated to that issue (aside from `mean` not erroring).
+        # The specified left truncation at 0 has no effect for `LogNormal`
+        d1 = truncated(LogNormal(1, 5), 0, 1e5)
+        @test mean(d1) ≈ 0 atol=eps()
+        v1 = var(d1)
+        @test v1 ≈ 0 atol=eps()
+        # Without a `max(_, 0)`, this would be within machine precision of 0 (as above) but
+        # numerically negative, which could cause downstream issues that assume a nonnegative
+        # variance
+        @test v1 >= 0
+        # Compare results with not specifying a lower bound at all
+        d2 = truncated(LogNormal(1, 5); upper=1e5)
+        @test mean(d1) == mean(d2)
+        @test var(d1) == var(d2)
+
+        # Truncated outside of support where taking a log would error
+        d3 = truncated(LogNormal(); lower=-1)
+        @test mean(d3) == mean(d3.untruncated)
+    end
+end

test/truncated/normal.jl

@@ -69,3 +69,37 @@ end
         @test isfinite(pdf(trunc, x))
     end
 end
+
+@testset "Truncated normal MGF" begin
+    two = big(2)
+    sqrt2 = sqrt(two)
+    invsqrt2 = inv(sqrt2)
+    inv2sqrt2 = inv(two * sqrt2)
+    twoerfsqrt2 = two * erf(sqrt2)
+
+    for T in (Float32, Float64, BigFloat)
+        d = truncated(Normal{T}(zero(T), one(T)), -2, 2)
+        @test @inferred(mgf(d, 0)) == 1
+        @test @inferred(mgf(d, 1)) ≈ @bigly sqrt(ℯ) * (erf(invsqrt2) + erf(3 * invsqrt2)) / twoerfsqrt2
+        @test @inferred(mgf(d, 2.5)) ≈ @bigly exp(25//8) * (erf(9 * inv2sqrt2) - erf(inv2sqrt2)) / twoerfsqrt2
+    end
+
+    d = truncated(Normal(3, 10), 7, 8)
+    @test mgf(d, 0) == 1
+    @test mgf(d, 1) == 0
+
+    d = truncated(Normal(27, 3); lower=0)
+    @test mgf(d, 0) == 1
+    @test mgf(d, 1) ≈ @bigly 2 * exp(63//2) / (1 + erf(9 * invsqrt2))
+    @test mgf(d, 2.5) ≈ @bigly 2 * exp(765//8) / (1 + erf(9 * invsqrt2))
+
+    d = truncated(Normal(-5, 1); upper=-10)
+    @test mgf(d, 0) == 1
+    @test mgf(d, 1) ≈ @bigly erfc(3 * sqrt2) / (exp(9//2) * erfc(5 * invsqrt2))
+
+    @test isnan(mgf(truncated(Normal(); upper=NaN), 0))
+
+    @test mgf(truncated(Normal(), -Inf, Inf), 1) == mgf(Normal(), 1)
+
+    @test mgf(truncated(Normal(), 2, 2), 1) == exp(2)
+end