  Model counting is a fundamental problem in many practical applications,
including query evaluation in probabilistic databases and failure-probability
estimation of networks. In this work, we focus on a variant of this problem
where the underlying formula is expressed in the Disjunctive Normal Form (DNF),
also known as #DNF. This problem has been shown to be #P-complete, making it
often intractable to solve exactly. Much research has therefore focused on
obtaining approximate solutions, particularly in the form of $(\varepsilon,
\delta)$ approximations.
  The primary contribution of this paper is a new approach, called pepin, an
approximate #DNF counter that significantly outperforms prior state-of-the-art
approaches. Our work is based on the recent breakthrough in the context of the
union of sets in the streaming model. We demonstrate the effectiveness of our
approach through extensive experiments and show that it provides an affirmative
answer to the challenge of efficiently computing #DNF.
