I am looking for an approximation for Poisson binomial distribution:
The Poisson binomial distribution is the discrete probability distribution of a sum of $n$ independent Bernoulli trials. you can find its pdf in http://en.wikipedia.org/wiki/Poisson_binomial_distribution
In addition, you can find 2 methods for it in the mentioned link. But when I use the second method (using Fourier Transform), the result would be an imaginary number.
I also wanted to use approximations in the following paper
http://statistics.stanford.edu/~ckirby/techreports/ONR/SOL%20ONR%20467.pdf
but the approximations are not clear to me. I would be grateful if somebody explains to me:
1) why do I get imaginary number using the second method ( Fourier transform)?
2) and also for example, how are the probabilities in Table 2 in the paper calculated?
Best Answer
1) Post the script/code/etc. you used, there is an error in it that perhaps readers can help you with: I whipped up the example in my CAS and it gets proper answer within the precision set.
2) The examples in that paper use multiples for each Pn, i.e., in its first example with comparisons in table 2, we have Pn of [.02,.04,.06,.08,.10], but there are 5 each accounted for, so you need to apply the calculation to the list of probabilities consisting of five repetitions of each value. Same with later examples. The result tables then show the true value and the various approximations that S(actual expected successes)<= s for some number of s successes, so you need to sum from 0 to s in your calculations to compare your results.
As previous posters stated, the Wikipedia DFT form is not an approximation, it is exact (modulo the capability of your computing system). It will take some serious cycles with large arguments - the form lends itself to using DFT tricks to speed calculations, but if you just implement it "as is" in say a CAS, you're not gaining much efficiency if any over the recursive form sans call stack space.