Implicit probabilistic models are a very flexible class for modeling data. They define a process to simulate observations, and unlike traditional models, they do not require a tractable likelihood function. In this paper, we develop two families of models: hierarchical implicit models and deep implicit models. They combine the idea of implicit densities with hierarchical Bayesian modeling and deep neural networks. The use of implicit models with Bayesian analysis has in general been limited by our ability to perform accurate and scalable inference. We develop a variational inference algorithm for implicit models. Key to our method is specifying a variational family that is also implicit. This matches the model's flexibility and allows for accurate approximation of the posterior. Our method scales up implicit models to sizes previously not possible and opens the door to new modeling designs. We demonstrate diverse applications: a large-scale physical simulator for predator-prey populations in ecology; a Bayesian generative adversarial network for discrete data; and a deep implicit model for text generation.

本文介绍了一种基于隐式概率模型的变分推理算法，称为 LFVI。我们介绍了一种称为HIEMS的隐式概率模型，这种模型将隐含密度与分层贝叶斯建模相结合，可以处理具有丰富隐藏结构的数据。通过与模型的灵活性匹配来构建隐含变分族，从而实现了后验的准确近似。我们展示了 LFVI 的多种应用：在生态学中用于生态中的捕食-被食模拟器，在离散数据的贝叶斯对抗生成网络中以及在文本生成中的一个隐式模型。

分层隐式模型与无似然变分推断