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The PASTA language leverages PASP to express statistical statements. A solver with the same name allows to perform inference in PASTA programs and, in general, in PASP. In this paper, we investigate inference in PASP, propose a new inference algorithm called aspcs based on Second Level Algebraic Model Counting (2AMC), and implement it into the aspmc solver. Then, we compare it with PASTA on a set of benchmarks: the empirical results show that, when the program does not contain aggregates, the new algorithm outperforms PASTA. 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