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首页> 外文会议>Annual ACM/IEEE Symposium on Logic in Computer Science >Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints
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Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints

机译：概率编程的语义：高阶函数，连续分布和软约束

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摘要

We study the semantic foundation of expressive probabilistic programming languages, that support higher-order functions, continuous distributions, and soft constraints (such as Anglican, Church, and Venture). We define a metalanguage (an idealised version of Anglican) for probabilistic computation with the above features, develop both operational and denotational semantics, and prove soundness, adequacy, and termination. This involves measure theory, stochastic labelled transition systems, and functor categories, but admits intuitive computational readings, one of which views sampled random variables as dynamically allocated read-only variables. We apply our semantics to validate nontrivial equations underlying the correctness of certain compiler optimisations and inference algorithms such as sequential Monte Carlo simulation. The language enables defining probability distributions on higher-order functions, and we study their properties.Categories and Subject Descriptors CR-number [D.3]: Programming languages.
机译：我们研究表达性概率编程语言的语义基础，该语言支持高阶函数，连续分布和软约束（例如英国国教，教堂和创业）。我们定义具有上述功能的概率计算的元语言（英国国教的理想版本），开发操作和指称语义，并证明其合理性，充分性和终止性。这涉及度量理论，随机标记的过渡系统和函子类别，但允许直观的计算读数，其中之一将采样的随机变量视为动态分配的只读变量。我们应用语义来验证一些编译器优化和推理算法（例如顺序蒙特卡洛模拟）的正确性背后的非平凡方程式。该语言可以定义高阶函数的概率分布，并且我们研究它们的属性。类别和主题描述符CR编号[D.3]：编程语言。

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