具有推理组合器的概率规划的学习建议
Learning Proposals for Probabilistic Programs with Inference Combinators
摘要：
我们开发的运营商建设的建议，在概率程序，我们称为推理组合。推理组合子定义了一个语法上的重要性采样组成的基本操作，如应用程序的过渡内核和重要性restaurant。这些采样器中的建议可以使用神经网络进行参数化，而神经网络又可以通过优化变分目标进行训练。其结果是用户可编程的变分方法，是正确的建设，并可以定制特定的模型的框架。我们证明了这个框架的灵活性，通过实施先进的变分方法的基础上摊销吉布斯抽样和退火。
英文摘要：
We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. Inference combinators define a grammar over importance samplers that compose primitive operations such as application of a transition kernel and importance resampling. Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives. The result is a framework for user-programmable variational methods that are correct by construction and can be tailored to specific models. We demonstrate the flexibility of this framework by implementing advanced variational methods based on amortized Gibbs sampling and annealing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
