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An Abstract Monte-Carlo Method for the Analysis of Probabilistic Programs
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We introduce a new method, combination of random testing and abstract interpretation, for the analysis of programs featuring both probabilistic and non-probabilistic nondeterminism. After introducing "ordinary" testing, we show how to combine testing and abstract interpretation and give formulas linking the precision of the results to the number of iterations. We then discuss complexity and optimization issues and end with some experimental results.
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Cited by 1 Pith paper
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Compositional Inference Metaprogramming with Convergence Guarantees
Introduces independent subproblem inference and proves asymptotic convergence guarantees for hybrid MCMC algorithms defined via inference metaprogramming.
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