Talk:Discrete uniform distribution
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There's quite a lot in this article that I would not buy into. The restriction that parameters and points of support be integers is not necessary. Rather, n equally likely events can be inscribed into any interval, and the formula n=b-a+1 then no longer holds.
Also, the statement that "The convention is used that the cumulative mass function Fk(ki) is the probability that k > = ki" seems mistaken, the correct version being "The convention is used that the cumulative mass function Fk(ki) is the probability that k < = ki". —Preceding unsigned comment added by 184.108.40.206 (talk) 05:37, 11 March 2006 (UTC)
Mean and Variance
Shouldn't both of these be the sum of ni/n, (that is, the sum of the point values divided by the number of points)? (a+b)/2 only works if you have a discrete uniform distribution with only two points. —Preceding unsigned comment added by Beefpelican (talk • contribs) 14:33, 16 November 2009 (UTC)
Note the paragraph at top which reads: "In case the values of a random variable with a discrete uniform distribution are real, it is possible to express the cumulative distribution function in terms of the degenerate distribution; thus"
1) I believe I know what it's trying to say, but it's wildly ambiguous. A simple syntactic rewrite would make this much clearer, as in "When a random variable has discrete values which are not integers..."
2) Since this is an expansion of the original thought to real-valued discrete variables, perhaps the original (simpler) thought should just be continued; ie - put this paragraph further down the article after the discussion about integer values has been more fully developed. —Preceding unsigned comment added by 220.127.116.11 (talk) 14:25, 11 April 2010 (UTC)
I'm wondering why the 'Notation' and 'Parameters' parts don't print as they appear on the page; everything else prints OK.
Distribution of sums of discrete uniform random variables
Mathworld.Wolfram (http://mathworld.wolfram.com/Dice.html) describes the distributions which arise from the sum of dice rolls. I feel as if the information needs to be condensed and transferred in a copyright respecting format.
Here is my take at deriving the distribution of independent sums of discrete uniform random variables:
For s-sided dice each independent and , summing over each additional die performs discrete convolution
Then it is a straightforward case of induction to show that pmf described on http://mathworld.wolfram.com/Dice.html fulfills the above recursion (assuming I avoided making mistakes).Mouse7mouse9 04:28, 24 February 2015 (UTC)