We study the problem of estimating the number of hyperedges in an arbitrary $n$-vertex hypergraph using sublinear in $n$ queries. Note that the number of hyperedges, $m$, can be exponential in $n$. For $k$-uniform hypergraphs, estimating $m$ is equivalent to estimating the average vertex degree, a problem studied in Barhum's Master's thesis (Weizmann Inst., 2007) under the standard access model of sampling random vertices, querying vertex degrees, and accessing incident hyperedges. Barhum's techniques do not extend to arbitrary hypergraphs, and simple lower-bound examples show that the standard access model cannot yield strongly sublinear algorithms when hyperedges have unbounded size. To obtain non-trivial sublinear bounds, we consider a natural generalization of the access model called the \emph{dual access model}, which allows sampling (labels of) random hyperedges, querying edge sizes, and accessing vertices in a hyperedge. In this model, we give a randomized algorithm that returns a $(1+\varepsilon)$-approximation to $m$ with high probability, making $O(\varepsilon^{-2}\sqrt{n} + \sqrt{n}\log n)$ queries. Complementing our algorithm, we prove a nearly matching lower bound showing that $Ω(\sqrt{n})$ queries are necessary for any algorithm that obtains a constant factor approximation to $m$.

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