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Our main result is designing an algorithm that returns a vertex cover of$\mathcal{G}^\star$ with size at most $(3/2+\epsilon)$ times the expected sizeof the minimum vertex cover, using only $O(n/\epsilon p)$ non-adaptive queries.This improves over the best-known 2-approximation algorithm by Behnezhad, Blum,and Derakhshan [SODA'22], who also show that $\Omega(n/p)$ queries arenecessary to achieve any constant approximation.  Our guarantees also extend to instances where edge realizations are not fullyindependent. We complement this upper bound with a tight $3/2$-approximationlower bound for stochastic graphs whose edges realizations demonstrate mildcorrelations.

Stochastic Minimum Vertex Cover in General Graphs: a $3/2$-Approximation