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The CARBS project develops a mathematical framework for approximate reasoning in probabilistic programming languages. It focuses on compositional proof techniques applicable to various quantitative models, particularly in analyzing network protocols' performance metrics like failure rates and throughput.
Programming languages with probabilistic features are used extensively in computer science and beyond, to model uncertainty, perform quantitative analysis, inference and much more.
To analyse programs in such languages, it is essential to have effective tools and techniques for approximate reasoning: for instance, determining the chance of congestion in a network, or the chance of failure of a system component. CARBS proposes a general mathematical framework of compositional proof techniques for…
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