[mbauman]

Oh I completely agree there's value in improving the accuracy of the cdf, especially for 32-bit float! There it can be quite a surprise that tests like `random() < x` can be off by as much as .6% for values of x on the order of 1f-5 and 6% for values on the order of 1f-6, even though they are an order of magnitude or two over `eps(1f0)`.

Following your post, I've found the following fast and straightforward and SIMD-friendly implementation that uses all 64 bits for a [0, 1) distribution:

    ```julia
    function random_float(rng)
        r = rand(rng, UInt64)
        last_bit = r & -r
        exponent = UInt64(2045)<<52 - reinterpret(UInt64, Float64(last_bit))
        exponent *= !iszero(r)
        fraction = ((r ⊻ last_bit)>>(8*sizeof(UInt64) - 52)) % UInt64
        return exponent | fraction
    end
    ```