# Poisson binomial distribution PMF conjecture

I have an intuition that re-casting the Poisson binomial distribution probability mass function in the following way might lead to a more elegant closed form than the DFT approach:

Sum the $\log_2$'s of the probabilities of each independent trial. The negation of that sum is equivalent to a new number of trials each of which has a $p=1/2$. Of course, if all the $p$'s start as $1/2$ the number of trials will be unchanged and one can degenerate the existing formulas accordingly to yield an $O(n)$ solution. But if the $p$'s differ, something else must be done (for example, because not every $p=1/2$ trial is independent).

My conjecture (or perhaps "wishful thinking" is more appropriate) is that there is an $O(n)$ closed form which will tell how one must re-number the desired number of successful trials ($k$) so it can be applied to the new number of trials where each has a $p$ of $1/2$, but not all are independent, yielding the same result as one would get from the ordinary PBD pmf.

• How is the new space going to have the same support as the original PBD $\{0, \dotsc, n\}$ if there are fewer trials? – Neil G Feb 29 '12 at 19:39

At a glance, if you have n trials, then the parameter space of n independent trials is dimension n (each p_n is a separate parameter). But for the not-independent trials, you have an effective dimension of (2^n)-n-1. Each combination of pass/fail is a separate nonnegative parameter. The probability of all such states much add to 1 and the conditional probabilities of the states where the ith trial passes is set equal to 1/2. That gives n+1 constraints on 2^n parameters.

So my view is that there are enough parameters to do something like what you want for n>2. The key remaining problem is coming up with a transformation of the system of independent trials into the latter. I don't have any ideas there.

• As an aside, you might want to consider the n=3 or n=4 case. If there are interesting transformations at these sizes, then maybe they can be generalized to arbitrary n. – Karl Hallowell Oct 2 '11 at 13:43