  We consider a seller faced with buyers which have the ability to delay their
decision, which we call patience. Each buyer's type is composed of value and
patience, and it is sampled i.i.d. from a distribution. The seller, using
posted prices, would like to maximize her revenue from selling to the buyer. In
this paper, we formalize this setting and characterize the resulting
Stackelberg equilibrium, where the seller first commits to her strategy, and
then the buyers best respond. Following this, we show how to compute both the
optimal pure and mixed strategies. We then consider a learning setting, where
the seller does not have access to the distribution over buyer's types. Our
main results are the following. We derive a sample complexity bound for the
learning of an approximate optimal pure strategy, by computing the
fat-shattering dimension of this setting. Moreover, we provide a general sample
complexity bound for the approximate optimal mixed strategy. We also consider
an online setting and derive a vanishing regret bound with respect to both the
optimal pure strategy and the optimal mixed strategy.
