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.

翻译：我们开发了概率程序建议构建操作器,我们称之为推论相交集器。推论相汇器为构成原始操作的重要取样器定义了语法图,例如应用过渡内核和重新采样。这些采样器中的提案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异目标进行培训。结果为用户可编程变异方法提供了一个框架,通过构建可以正确无误,并且可以适应特定模型。我们通过在摊销Gibbs取样和肛门取样的基础上采用先进的变异方法,展示了这一框架的灵活性。