's in the binary representation
of the words. The precision obtained is around 
for a memory
requirement of 10kbits. The analysis involves maxima of geometric
random variables and Mellin transform techniques.
Probabilistic counting algorithms estimate approximately the number of
different words in a very large collection of data in a single pass
using a very small memory. We consider a modification of the
Probabilistic Counting algorithm of Flajolet and
Martin [FM85]. This is based on the observation of
occurrences of initial sequences of 's in the binary representation
of the words. The precision obtained is around for a memory
requirement of 10kbits. The analysis involves maxima of geometric
random variables and Mellin transform techniques.