How to test that two vectors of histogram counts over the same bins come from the same multinomial distribution Preamble:
When testing goodness of fit with null distribution with atoms using an EDF-based statistics $T_n$, random variable $n\sqrt{T_n}$ converges in law. 
Empirically, the law observed with absolutely continuous null is different, than with purely discrete null. 
I was assessing whether, given position of atoms (say integers 0 through 10), the distribution is the same for different weight assignments. So I generated samples from different discrete distributions, computed KS statistics, binned it, and was trying to compare counts to see if I can detect statistically significant differences.
Using Mathematica:
KolmogorovSmirnovStatisticFiniteBoundDiscrete[
   vec_ /; VectorQ[vec, IntegerQ], 
   {kmin_Integer, kmax_Integer}, 
   pvec_
] /; Min[pvec] >= 0 && Total[pvec] == 1 :=
Module[{cnt, pdfList, ecdf, ncdf},
  cnt = KeySort[Counts[vec]];
  pdfList = AssociationThread[Range[kmin, kmax] -> pvec];
  ecdf = Accumulate[
        Normalize[Values[KeySort[Merge[{cnt, 0 pdfList}, Total]]], Total]];
  ncdf = Accumulate[Values[pdfList]];
  Max[
     Abs[Most[ecdf] - Most[ncdf]],
     If[First[Keys[cnt]] == kmin, 0, 
         Part[ncdf, First[Keys[cnt]] - kmin]],
     If[Last[Keys[cnt]] == kmax, 0, 
         1 - Part[ncdf, First[Keys[cnt]] - kmin + 1]]
   ]
]

Generate KS statistics for discrete uniform nulls on $[0,5]$
With[{dist = DiscreteUniformDistribution[{0, 5}], n = 1600},
 stat1 = Sqrt[n] Table[
     KolmogorovSmirnovStatisticFiniteBoundDiscrete[
      RandomVariate[dist, n], {0, 5}, 
      PDF[dist][Range[0, 5]]], {100000}];]

Generate KS statistics for binomial null $\mathrm{Bin}\left(5,\frac{2}{3}\right)$
With[{dist = BinomialDistribution[5, 2/3], n = 1600},
 stat2 = Sqrt[n] Table[
     KolmogorovSmirnovStatisticFiniteBoundDiscrete[
      RandomVariate[dist, n], {0, 5}, 
      PDF[dist][Range[0, 5]]], {100000}];]

Plot histograms of statistics under different nulls:
Histogram[{stat1, stat2}, Automatic, "Probability", 
 ChartLegends -> {"DiscreteUniform", "Binomial"}]


These statistics have discrete values, for finite $n$, but in the large $n$ limit their cumulative distribution function should converge to an asymptotic continuous CDF.
I was investigating whether these limiting laws are going to be different for different discrete nulls, given both have the same support, as in the above example.
For this I partitioned the positive semi-axis into disjoint intervals, and recorded counts for statistics values in these experiments.
For example:
In[232]:= HistogramList[stat1, {{0, 0.4, 0.9, 100}}]

Out[232]= {{0, 0.4, 0.9, 100}, {19528, 65074, 15398}}

In[233]:= HistogramList[stat2, {{0, 0.4, 0.9, 100}}]

Out[233]= {{0, 0.4, 0.9, 100}, {32064, 58338, 9598}}

Now, given vectors of integer counts, under the null hypothesis that they arose as samples of the same unknown multinomial distribution, I would like to check if data contain evidence against it.

Hence my question:
I have two independent datasets of equal size, and for each of them I compute bin-counts for the common bins.
I hence get two multinomial samples $\{r_1, \ldots, r_n\}$ and $\{t_1, \ldots, t_n\}$. My null hypothesis is that these vectors are samples from the same population distribution. 
What test would be appropriate to assess whether differences between $R$ and $T$ vectors can be attributed to randomness. 
I am interested in the math part of the question, rather than in actual test computation. 
Thank you.
P.S. Feel free to respond using R, Python or any other software package.
 A: What you're trying to can be thought of as a contingency table test, I think.
Imagine a matrix. Each column is one of your bins (so, $n$ columns). Each row is one of your generating distributions (so, 2 rows). Your null hypothesis is that the rows and columns are independent--that the probability of being in row 2, column 3, is the probability of being in row 2 (1/2, assuming you generated equal numbers form each), times the probability of being in column 3 (which we hypothesize is independent of the row).
We decide beforehand how many are going to be in each row, so we have fixed row marginals. We do not decide how many are going to be in each column, so we don't have fixed column marginals. So what I usually use for contingency tables, Fisher's exact test, is I guess not really applicable--it assumes fixed row and column marginals. If you want ot try it anyway, it's fisher.test in R.
There's also the chi-squared contingency table, chisq.test in R. I don't know what the assumptions of this test are, though.
