Computation of the expected value of a multidimensional array providing PMF on each dimension

General Usage
1

Isn’t there something smarter already in the Julia standard lib to compute the expected value of a multidimensional tensor given a vector of PMFs than the following code ?

julia> function EV(v,p) # v is a multidimensional array, p is a vector of vectors (PMFs, one per dimension)
           (ndims(v) == length(p) && all(size(v) .== length.(p))) || error("Mismatch dimension or size between the value tensor and the probability vectors")
           (all([all(>=(0-eps()), v) for v in p])  && all(sum.(p) .> 1 .- eps() ) && all(sum.(p) .> 1 .- eps() )) || error("p is not a vector of probabilities")
           outsum = 0.0
           comp   = 0.0 #https://en.wikipedia.org/wiki/Kahan_summation_algorithm
           nd = ndims(v)
           for idx in CartesianIndices(size(v)) 
               y = *(v[idx],[p[d][idx[d]] for d in 1:nd]...) - comp
               t = outsum + y
               comp = (t - outsum) - y
               outsum = t
           end
           return outsum
       end
EV (generic function with 1 method)

julia> v = collect(reshape(1:12,(3,2,2)));

julia> p = [[0.5,0.3,0.2],[0.6,0.4],[0.8,0.2,]];

julia> EV(v,p)
4.1000000000000005

julia> v = [2;2;;2;2;;;4;4;;4;4;;;]
2×2×2 Array{Int64, 3}:
[:, :, 1] =
 2  2
 2  2

[:, :, 2] =
 4  4
 4  4

julia> p = [[0.5,0.5],[0.5,0.5],[0.8,0.2]]
3-element Vector{Vector{Float64}}:
 [0.5, 0.5]
 [0.5, 0.5]
 [0.8, 0.2]

julia> EV(v,p)
2.4000000000000004