论文标题
多模型概率编程
Multi-Model Probabilistic Programming
论文作者
论文摘要
概率编程使代表概率模型作为程序变得容易。但是，建立单个模型只是概率建模的一步。概率建模的更广泛挑战在于理解和导航替代模型的空间。尽管它们的核心作用，但目前没有很好的方式来代表这些替代模型的这些空间。我们提出了概率编程的扩展，使每个程序都代表一个相互关联的概率模型网络。我们为这些多模型概率程序提供了正式的语义，用于模型网络操作的有效算法的集合以及在流行的概率编程语言STAN之上构建的示例实现。该模型网络表示形式开放了许多大门，包括模型空间中的搜索和自动化，模型开发的跟踪和通信以及明确的建模者自由度，以减轻诸如P-Hacking之类的问题。我们使用Stan实施来展示自动模型搜索和模型开发跟踪，并提出了更多可能的应用程序。
Probabilistic programming makes it easy to represent a probabilistic model as a program. Building an individual model, however, is only one step of probabilistic modeling. The broader challenge of probabilistic modeling is in understanding and navigating spaces of alternative models. There is currently no good way to represent these spaces of alternative models, despite their central role. We present an extension of probabilistic programming that lets each program represent a network of interrelated probabilistic models. We give a formal semantics for these multi-model probabilistic programs, a collection of efficient algorithms for network-of-model operations, and an example implementation built on top of the popular probabilistic programming language Stan. This network-of-models representation opens many doors, including search and automation in model-space, tracking and communication of model development, and explicit modeler degrees of freedom to mitigate issues like p-hacking. We demonstrate automatic model search and model development tracking using our Stan implementation, and we propose many more possible applications.
