We develop a new formulation of Stein’s method to obtain computable upper bounds on the total variation
distance between the geometric distribution and a distribution of interest. Our framework reduces the
problem to the construction of a coupling between the original distribution and the “discrete equilibrium”
distribution from renewal theory.We illustrate the approach in four non-trivial examples: the geometric sum
of independent, non-negative, integer-valued random variables having common mean, the generation size
of the critical Galton–Watson process conditioned on non-extinction, the in-degree of a randomly chosen
node in the uniform attachment random graph model and the total degree of both a fixed and randomly
chosen node in the preferential attachment random graph model.
Keywords: discrete equilibrium distribution; geometric distribution;