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Explore a 17-minute video presentation from the PLDI 2023 conference that delves into the automated inference of expected result values in probabilistic programs with complex structures. Learn about a novel approach to analyzing recursive programs, procedures, and local variables using a term representation called infer[.]. Discover how this methodology translates pre-expectation semantics into first-order constraints, enabling automation through standard methods. Gain insights into the use of logical variables inspired by Hoare logics for recursive programs, and understand how this technique extends beyond tail-recursion. Examine the implementation of this analysis in the ev-imp prototype and review experimental evidence demonstrating its algorithmic expressibility. Access supplementary materials, including available and reusable artifacts, to further explore this innovative research in probabilistic programming and expected value analysis.
