  Memory becomes a limiting factor in contemporary applications, such as
analyses of the Webgraph and molecular sequences, when many objects need to be
counted simultaneously. Robert Morris [Communications of the ACM, 21:840--842,
1978] proposed a probabilistic technique for approximate counting that is
extremely space-efficient. The basic idea is to increment a counter containing
the value $X$ with probability $2^{-X}$. As a result, the counter contains an
approximation of $\lg n$ after $n$ probabilistic updates stored in $\lg\lg n$
bits. Here we revisit the original idea of Morris, and introduce a binary
floating-point counter that uses a $d$-bit significand in conjunction with a
binary exponent. The counter yields a simple formula for an unbiased estimation
of $n$ with a standard deviation of about $0.6\cdot n2^{-d/2}$, and uses
$d+\lg\lg n$ bits.
  We analyze the floating-point counter's performance in a general framework
that applies to any probabilistic counter, and derive practical formulas to
assess its accuracy.
