标题： 自动微分变分推断 显示英文标题

标题： Automatic Differentiation Variational Inference

摘要： 概率建模是迭代的。一名科学家提出一个简单的模型，将其拟合到她的数据上，根据分析对其进行 refinement（改进），然后重复这一过程。然而，将复杂模型拟合到大量数据上是这一过程中的一大瓶颈。为新模型推导算法可能在数学和计算上都具有挑战性，这使得高效地循环通过这些步骤变得困难。为此，我们开发了自动微分变分推理（ADVI）。使用我们的方法，科学家只需提供一个概率模型和一个数据集，无需其他操作。ADVI 自动推导出一个有效的变分推理算法，使科学家能够改进和探索许多模型。ADVI 支持广泛的模型类别——不需要共轭假设。我们研究了十个不同的模型，并将其应用于一个包含数百万观测值的数据集。ADVI 集成到了 Stan 中，这是一个概率编程系统；它可供立即使用。 显示英文摘要

摘要： Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines it according to her analysis, and repeats. However, fitting complex models to large data is a bottleneck in this process. Deriving algorithms for new models can be both mathematically and computationally challenging, which makes it difficult to efficiently cycle through the steps. To this end, we develop automatic differentiation variational inference (ADVI). Using our method, the scientist only provides a probabilistic model and a dataset, nothing else. ADVI automatically derives an efficient variational inference algorithm, freeing the scientist to refine and explore many models. ADVI supports a broad class of models-no conjugacy assumptions are required. We study ADVI across ten different models and apply it to a dataset with millions of observations. ADVI is integrated into Stan, a probabilistic programming system; it is available for immediate use.