Modular probabilistic models via algebraic effects
Modular probabilistic models via algebraic effects
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DOI：
10.1145/3547635
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发表时间：
2022-03
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通讯作者：
Minh Nguyen;Roly Perera;M. Wang;Nicolas Wu
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作者：
Minh Nguyen;Roly Perera;M. Wang;Nicolas Wu
概率编程语言（Probabilistic Programming Language，PPL）允许程序员构建统计模型，然后模拟数据或对其进行推理。许多PPL将模型限制在模拟或推理的特定实例中，限制了它们的可重用性。在其他PPL中，模型不容易组合。使用Haskell作为宿主语言，我们提出了一个嵌入式领域的特定语言的基础上代数效应，概率模型是模块化的，一流的，可重用的模拟和推理。我们还演示了如何模拟和推理可以自然地表示为可组合的程序转换使用代数效果处理程序。
Probabilistic programming languages (PPLs) allow programmers to construct statistical models and then simulate data or perform inference over them. Many PPLs restrict models to a particular instance of simulation or inference, limiting their reusability. In other PPLs, models are not readily composable. Using Haskell as the host language, we present an embedded domain specific language based on algebraic effects, where probabilistic models are modular, first-class, and reusable for both simulation and inference. We also demonstrate how simulation and inference can be expressed naturally as composable program transformations using algebraic effect handlers.