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For a positive-integer geometric distribution $P(N=j)=p{q}^{j-1}$ with $q=1-p$, the capped time has masses $p{q}^{j-1}$ for $1\le j<k$ and $q^{k-1}$ at $k$. The last mass includes all later arrivals, not only $N=k$. Its probability generating function and expected value areThe expectation follows either by differentiating the PGF or summing the tail probabilities up to $k$.

For uniform independent draws of $n$ coupon types, the waiting times with $r$ unseen types remaining are independent positive-support geometric distributions of parameter $r/n$. Summing their means gives $n{\sum }_{r=1}^{n}1/r$, and summing their variances gives the displayed formula. Independence follows from fresh draws after each discovery and the fact that the next stage's conditional distribution depends only on the count of remaining types.

For a geometric distribution count on the positive integers, independent of independent and identically distributed random variables $X_i$, the moment-generating function of $S={\sum }_{i=1}^N{X}_i$ is the displayed expression. Condition on the count and sum a geometric series. For real $t$, the finite domain requires $M_X(t)<\infty$ and $(1-p)M_X(t)<1$. Positive summands always permit $t\le 0$, even when no positive exponential moment exists; this gives a Laplace transform of a nonnegative random variable. The aggregate has no mass at zero when all summands are strictly positive.

Let the summands have gamma distribution with shape two and rate $\beta$, and let the independent positive-support geometric distribution count have parameter $p$. Put $a=\beta (1-\sqrt{1-p})$ and $b=\beta (1+\sqrt{1-p})$. The aggregate moment-generating function factors as $ab/((a-t)(b-t))$, so it is the sum of two independent exponential distributions of rates $a,b$. Its probability density function is $ab(e^{-as}-e^{-bs})/(b-a)$ for $s>0$, nonnegative and integrating to one. This contrasts with the geometric sum of exponential variables, which is itself exponential.

For exponential rate $\lambda >0$, the integer part has mass $(1-e^{-\lambda })e^{-\lambda m}$ on nonnegative integers. The fractional part has density $\lambda e^{-\lambda u}/(1-e^{-\lambda })$ for $0\le u<1$. The mixed joint density factors into those marginals, proving independence. The integer part is a geometric distribution on zero-based support, with expectation $1/(e^{\lambda }-1)$.

Summing the stationary law of a linear flow network over its through-flow count gives independent local geometric distributions with ratios $q_i={\rho }_i/(1-{\rho }_0)$. This gives the displayed means. The local counts are independent of one another in this marginal distribution; the through count is generally dependent on them. The independence is a stationary distributional fact, not an assertion that their dynamics evolve independently.

Among distributions on $\{0,1,2,\dots \}$ with fixed expected value $\mu$, the geometric distribution with success probability $1/(1+\mu )$ uniquely maximizes information entropy. Its entropy is