diff --git a/src/MiscellaneousCopulas/FGMCopula.jl b/src/MiscellaneousCopulas/FGMCopula.jl
index e1f54727..a15a5bb4 100644
--- a/src/MiscellaneousCopulas/FGMCopula.jl
+++ b/src/MiscellaneousCopulas/FGMCopula.jl
@@ -16,22 +16,19 @@ C(\\boldsymbol{u})=\\prod_{i=1}^{d}u_i \\left[1+ \\sum_{k=2}^{d}\\sum_{1 \\leq j
 
 where `` \\bar{u}=1-u``.
 
-More details about Farlie-Gumbel-Morgenstern (FGM) copula are found in :
-    
-    Nelsen, Roger B. An introduction to copulas. Springer, 2006. Exercise 3.38.
-
-We use the stochastic representation of the copula to obtain random samples.
-    
-    Blier-Wong, C., Cossette, H., & Marceau, E. (2022). Stochastic representation of FGM copulas using multivariate Bernoulli random variables. Computational Statistics & Data Analysis, 173, 107506.
-
 It has a few special cases:
 - When d=2 and θ = 0, it is the IndependentCopula.
 
+More details about Farlie-Gumbel-Morgenstern (FGM) copula are found in [nelsen2006](@cite).
+We use the stochastic representation from [blier2022stochastic](@cite) to obtain random samples.
+
 References:
 * [nelsen2006](@cite) Nelsen, Roger B. An introduction to copulas. Springer, 2006.
+* [blier2022stochastic](@cite) Blier-Wong, C., Cossette, H., & Marceau, E. (2022). Stochastic representation of FGM copulas using multivariate Bernoulli random variables. Computational Statistics & Data Analysis, 173, 107506.
 """
-struct FGMCopula{d, Tθ} <: Copula{d}
+struct FGMCopula{d, Tθ, Tf} <: Copula{d}
     θ::Tθ
+    fᵢ::Tf
     function FGMCopula(d, θ)
         vθ = typeof(θ)<:Vector ? θ : [θ]
         if  all(θ .== 0)
@@ -40,70 +37,50 @@ struct FGMCopula{d, Tθ} <: Copula{d}
         # Check first restrictions on parameters
         any(abs.(vθ) .> 1) && throw(ArgumentError("Each component of the parameter vector must satisfy that |θᵢ| ≤ 1"))
         length(vθ) != 2^d - d - 1 && throw(ArgumentError("Number of parameters (θ) must match the dimension ($d): 2ᵈ-d-1"))
+        
         # Last check: 
-        rez = new{d, typeof(vθ)}(vθ)
         for epsilon in Base.product(fill([-1, 1], d)...)
-            if 1 + _reduce_over_combinations(rez,epsilon,prod) < 0
+            if 1 + _fgm_red(vθ, epsilon) < 0
                 throw(ArgumentError("Invalid parameters. The parameters do not meet the condition to be an FGM copula"))
             end
         end
-        return rez
+        
+        # Now construct the stochastic representation:
+        wᵢ = [_fgm_red(vθ, 1 .- 2*Base.reverse(digits(i, base=2, pad=d))) for i in 0:(2^d-1)]
+        fᵢ = Distributions.DiscreteNonParametric(0:(2^d-1), (1 .+ wᵢ)/2^d)
+        return new{d, typeof(vθ), typeof(fᵢ)}(vθ, fᵢ)
     end
 end
 Base.eltype(C::FGMCopula) = eltype(C.θ)
-function _reduce_over_combinations(C::FGMCopula{d,Tθ}, vector_to_combine, reducer_function) where {d,Tθ}
-    # This version of the reductor is non-allocative, which is much better in terms of performance. 
-    # Moreover, since $d$ is a type parameter the loop will fold out at compile time :)
-    rez = zero(eltype(vector_to_combine))
-    # Iterate over all possible combinations of k elements, for k = 2, 3, ..., d
-    i = 1
+function _fgm_red(θ, v)
+    # This function implements the reduction over combinations of the fgm copula. 
+    # It is non-alocative thus performant :)
+    rez, d, i = zero(eltype(v)), length(v), 1
     for k in 2:d
-      for indices in Combinatorics.combinations(1:d, k)
-        rez += C.θ[i] * reducer_function(vector_to_combine[indices])
-        i = i+1
-      end
+        for indices in Combinatorics.combinations(1:d, k)
+            rez += θ[i] * prod(v[indices])
+            i = i+1
+        end
     end
     return rez
 end
-function _cdf(fgm::FGMCopula, u::Vector{T}) where {T}
-    return prod(u) * (1 + _reduce_over_combinations(fgm, 1 .-u, prod))
-end
-function Distributions._logpdf(fgm::FGMCopula, u)
-    return log1p(_reduce_over_combinations(fgm, 1 .-2u, prod))
-end
-function Distributions._rand!(rng::Distributions.AbstractRNG, fgm::FGMCopula{d,Tθ}, x::AbstractVector{T}) where {d,Tθ, T <: Real}
-    if d == 2
-        u = rand(rng, T)
-        t = rand(rng, T)
-        a = 1.0 .+ fgm.θ .* (1.0-2.0*u)
-        b = sqrt.(a.^2 .-4.0 .*(a .-1.0).*t)
-        v = (2.0 .*t) ./(b .+ a)
-        x[1] = u
-        x[2] = v[1]
-        return x
-    elseif d > 2
-        I = zeros(T,d)
-        for i in 1:d
-            term = _reduce_over_combinations(fgm, I, x -> (-1)^sum(x))
-            I[i] = rand(rng) < (1 / 2^d) * (1 + term)
-        end
-        V0 = rand(rng, d)
-        V1 = rand(rng, d)
-        for j in 1:d
-            U_j = 1-sqrt(1-V0[j])*(1-V1[j])^(I[j])
-            x[j] = U_j
-        end
-        return x
-    end
+_cdf(fgm::FGMCopula, u::Vector{T}) where {T} = prod(u) * (1 + _fgm_red(fgm.θ, 1 .-u))
+Distributions._logpdf(fgm::FGMCopula, u) = log1p(_fgm_red(fgm.θ, 1 .-2u))
+function Distributions._rand!(rng::Distributions.AbstractRNG, fgm::FGMCopula{d, Tθ, Tf}, x::AbstractVector{T}) where {d,Tθ, Tf, T <: Real}
+    I = Base.reverse(digits(rand(rng,fgm.fᵢ), base=2, pad=d))
+    V₀ = rand(rng, d)
+    V₁ = rand(rng, d)
+    x .= 1 .- sqrt.(V₀) .* (V₁ .^ I)
+    return x
 end
-τ(fgm::FGMCopula) = (2*fgm.θ[1])/9
+τ(fgm::FGMCopula{2, Tθ, Tf}) where {Tθ,Tf} = (2*fgm.θ[1])/9
 function τ⁻¹(::Type{FGMCopula}, τ)
     if !all(-2/9 <= τi <= 2/9 for τi in τ)
         throw(ArgumentError("For the FGM copula, tau must be in [-2/9, 2/9]."))
     end
     return max.(min.(9 * τ / 2, 1), -1)
 end
-ρ(fgm::FGMCopula) = (1*fgm.θ)/3 # this is weird as it will return a vector ? 
+ρ(fgm::FGMCopula{2, Tθ, Tf}) where {Tθ,Tf} = fgm.θ[1]/3 
 function ρ⁻¹(::Type{FGMCopula}, ρ)
     if !all(-1/3 <= ρi <= 1/3 for ρi in ρ)
         throw(ArgumentError("For the FGM copula, rho must be in [-1/3, 1/3]."))