The first question to ask is: why do you want to “vectorize” the code in this way? Loops in Julia are fast, and if you have a working loop there is no need to change it into a map or broadcast call unless that helps you organize or structure your code more clearly.
Assuming that you do want to use map or broadcast, then you need to tell Julia to treat your prob not as a container of individual floats but as a collection of rows. For example, you could broadcast over eachrow(prob) rather than prob.
However, take a look at the answer by @stevengj in Some doubts about types - #6 by stevengj which also applies in your situation. It is common practice in Matlab or numpy to express a collection of vectors as a matrix, but that’s neither ideal nor necessary in Julia. For example, if you were to transpose your prob data and treat it as a collection of SVectors from StaticArrays, then you could easily broadcast over it:
julia> prob = [SVector(.4, .5, .9), SVector(.6, .5, .1)]
2-element Array{SArray{Tuple{3},Float64,1,3},1}:
[0.4, 0.5, 0.9]
[0.6, 0.5, 0.1]
julia> sum.(prob)
2-element Array{Float64,1}:
1.8
1.2000000000000002
There is no difference in memory layout between a matrix where each column is an element or a vector of SVectors representing those columns, but it is much easier to operate on each element if you actually represent your collection of vectors as a collection of vectors.