The binomial series gives, for ,
All coefficients are nonnegative, and . Thus define
These numbers sum to , so they define a probability distribution on the nonnegative integers with the desired probability generating function.
Differentiate the probability generating function:
Therefore
Using the relation between the second factorial moment and variance,
For a nonnegative random variable and , Markov inequality states
It follows by taking expectations in . A finite expectation makes the bound informative.
Since lies inside the radius of the probability generating function, its nonnegative series converges at , and
Apply Markov inequality to the nonnegative random variable . On it is at least , so
This exponential bound is substantially stronger here than applying Markov inequality directly to .

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