  Functional Distributional Semantics provides a computationally tractable
framework for learning truth-conditional semantics from a corpus. Previous work
in this framework has provided a probabilistic version of first-order logic,
recasting quantification as Bayesian inference. In this paper, I show how the
previous formulation gives trivial truth values when a precise quantifier is
used with vague predicates. I propose an improved account, avoiding this
problem by treating a vague predicate as a distribution over precise
predicates. I connect this account to recent work in the Rational Speech Acts
framework on modelling generic quantification, and I extend this to modelling
donkey sentences. Finally, I explain how the generic quantifier can be both
pragmatically complex and yet computationally simpler than precise quantifiers.
