The cumulative distribution function is
The total integral of the nonnegative probability density function is one.
For a random variable with a probability density function, a singleton has zero probability:
Equivalently, its cumulative distribution function is continuous, so it has no jump at .
The integers form a countable set, and the events are disjoint. By countable additivity and part ii,
Countability is essential: one cannot use the same sum argument over an arbitrary uncountable collection of zero-probability events.
Normalization of the probability density function requires
Hence . The distribution is a Pareto distribution with minimum one and shape parameter .
The condition guarantees a finite first moment. Direct integration gives
For an integer , is the same event as . Integration for the Pareto distribution gives
The second identity subtracts two successive tail probabilities, equivalently integrating over . If includes zero, the extra case is and , since almost surely.
For a nonnegative integer-valued random variable , the tail-sum formula states . It follows by writing and using nonnegativity to interchange sum and expectation.
With , this gives
The Riemann zeta function is finite here because .
By part iii, is an integer with probability zero. Thus almost surely, and
The pointwise inequalities imply
Putting and rearranging yields
This extracts bounds for an infinite series from simple rounding inequalities.

Articles by others on the same topic (0)

There are currently no matching articles.