Hello to all Mathematica Users! I have this question:

Consider a box which contains 69 balls. 17 red, 1 white, 29 blue and 22 green balls. There will be picked 7 balls randomly, without putting the balls back to the experiment.

My question is: How do I create a probability distribution of all the possible combinations and to visualize it? And then I would like to find the combination with the higher probability and the mean.

I know that I have to use "Multivariate Hypergeometric distribution", and I know how to compute the probability of a particular combination, but not how to find the more probable combination and the mean of the whole distribution.

  • $\begingroup$ Welcome to the forum. We appreciate it if you post whatever code you've come up with so far when posting questions -- makes it easier for us to help. $\endgroup$ – mfvonh Jun 26 '14 at 15:57

Define the distribution:

dist = MultivariateHypergeometricDistribution[7, balls = {17, 1, 29, 22}];

Just use the relevant functions for summary statistics:

Through[{Mean, Variance}[dist]] // N

{{1.72464, 0.101449, 2.94203, 2.23188}, {1.18505, 0.0911573, 1.55504, 1.38613}}

This is challenging to visualize because it is 4-dimensional. One option is to take some random draws and see what pattern they reveal:

  sample = RandomVariate[dist, 100]];
 ChartLayout -> "Stacked",
 ChartStyle -> {Red, White, Blue, Green},
 AspectRatio -> 1/5,
 ImageSize -> 500]

enter image description here

There are other ways to visualize this, of course, but we'd need to know more about your needs to make useful suggestions.

You'll need the PDF for probabilities:

pdf = PDF[dist, vars = {i, j, k, l}];

You can check the probability of any particular combination using rules (this is a piecewise function):

pdf /. {i -> 1, j -> 1, k -> 3, l -> 2} // N


And you can maximize this function to get the most likely combination:

MapAt[N, Maximize[pdf, vars, Integers], 1]

{0.106399, {i -> 2, j -> 0, k -> 3, l -> 2}}

| improve this answer | |
  • $\begingroup$ What you have written is quite interesting, but one thing that I don't understand is: Why we have to use the Probability Density Function if we are working with a Multivariate Discrete distribution? $\endgroup$ – user29859 Jun 27 '14 at 12:26

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