Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.

中文翻译：

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用于摊销推理的神经方法


在过去 50 年中，基于模拟的统计推理方法与技术进步保持同步，取得了长足的发展。该领域正在经历一场新的革命，因为它采用了神经网络、优化库和图形处理单元的表示能力，用于学习数据和推理目标之间的复杂映射。生成的工具是摊销的，因为在初始设置成本之后，它们允许通过快速前馈操作进行快速推理。在本文中，我们回顾了点估计、近似贝叶斯推理、汇总统计构造和似然近似方面的最新进展。我们还介绍了软件，并提供了一个简单的插图，以展示可用于摊销推理的各种工具，以及它们相对于马尔可夫链蒙特卡洛方法提供的优势。本文最后概述了相关主题并展望了未来的研究方向。