# most general way to generate pairwise independent random variables?

Is there a sort of structure theorem for pairwise independent random variables or a very general way to create them?

I'm wondering because I find it difficult to come up with a lot of examples of nontrivial pairwise independent random variables. (by 'nontrivial', i mean not mutually independent)

# one example (three r.v.):

X = face of dice 1

Y = face of dice 2

Z = X + Y mod 6

# another example (three events) from some book:

Throw three coins. A = the number of heads is even, B = the first two flips are the same, C = the second two flips are heads.

# another example:

$A_{ij}$ = dice i and dice j having the same face

($A_{ij}$, $i\neq j$) form a set of pairwise independent events, but the triple ($A_{ij}$, $A_{jk}$, $A_{ki}$) is not mutually independent.

-
I don't think this question is as elementary as the down voters seem to think. Here is a way of making it sound harder, while being no deeper: Fix finite sets A, B and C. Consider the set of all probability distributions on A x B x C for which every two variables are independent. Can this set be parameterized? For example, it is semi-algebraic; is the underlying variety unirational? – David Speyer Dec 6 '09 at 14:32
There is a large body of literature on constructions of k-wise independent random variables. I suggest to look at Alon and Spencer's book on the probabilistic method. – Gil Kalai Dec 6 '09 at 15:53
how can I check if an algorithm generates random variates of a specific distribution??? – user3216 Jan 13 '10 at 7:02
[The following is probably obvious stuff, but since this page doesn't yet mention the word "polynomial"…] One standard way to generate n pairwise independent random variables is to take some prime p greater than n, independently generate two values a and b modulo p (a≠0), and take values of the polynomial (ax+b) mod p for different x (say X_i=(ai+b) mod p, for i=0 to i=n). This is pairwise independent but not 3-wise independent. In general, to get random variables that are k-wise independent but not (k+1)-wise, take a polynomial of degree k-1. – shreevatsa Jun 28 '11 at 4:43

I'm sure that Gil's answer is wise and that it is a good idea to look at Alon and Spencer's book. Here also is a quick summary of what is going on.

Suppose that $X_1,\ldots,X_n$ are random variables, and suppose for simplicity that they take finitely many values. Suppose that you prescribe the distribution of each $X_i$, and suppose also that you want the random variables to be pairwise independent or $k$-wise independent. Then the constraints on the joint distribution are a finite list of equalities and inequalities. The solution set is a polytope whose dimension is fairly predictable, and the fully independent distribution is always in the interior of this polytope. If you are interested in $k$-wise independent distributions that are far from $k+1$-wise independent, then it can be difficult to determine what is achievable because the polytope is complicated. (The vertices are a particularly interesting and non-trivial class: $k$-wise independent distributions with small support. These are called "weighted orthogonal arrays".) However, if you're just intersted in examples, it is much easier to write down a small deviation of the fully independent distribution. The deviation just satisfies linear equations.

For example, suppose that $X,Y,Z$ are three unbiased Bernoulli random variables (coin flips) that take values $0$ and $1$. Then there are 8 probabilities $p_{ijk}$, one for each outcome $(X,Y,Z) = (i,j,k)$. Then you can set $$p_{ijk} = \frac18 + (-1)^{ijk}\epsilon. \qquad\qquad\qquad \text{(1)}$$ to get a pairwise independent but not independent distribution. In this simple example, there is a 1-dimensional space of deviations and it is easy to compute how far you can vary the independent solution. (Up to $|\epsilon| = \frac18$.) In larger cases, the variations can be multidimensional and the polytope of deviations can be more complicated.

Addendum: If I have not made a mistake, all deviations for any finite list of discrete random variables are linear combinations of those of the form (1). More precisely, given discrete random variables $X_1,\ldots,X_n$, let $f_i$ be some function of the value of $X_i$ which is 1 for one value, $-1$ for another value, and $0$ otherwise. Then you can make deviations proportional to $\prod f_{i_j}$ as long as there are at least $k+1$ factors. It looks like all deviations are a linear combination of those of this form.

-

One very useful construction: if $X_1,\ldots,X_n$ are i.i.d. RVs, uniform in $\{0,\ldots,q-1\}$ ($q$ prime), then two linear combinations $\sum a_i X_i$ and $\sum b_i X_i$ are independent iff the vectors $a$ and $b$ are linearly independent (all operations are modulo q).

If we take $q=2$, this means that using $n$ i.i.d unbiased coin flips we can generate $2^n-1$ pairwise independent RVs, by taking all nonzero linear combinations.

This can be further generalized and generally yields some connections between k-wise independence and error correcting codes.

-