Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high dimensional models with many observations. In this article we discuss the potential to improve posterior computation using ideas from machine learning. Concrete future directions are explored in vignettes on normalizing flows, Bayesian coresets, distributed Bayesian inference, and variational inference.

翻译：贝叶斯模型是研究复杂数据的有力工具，它允许分析人员编码丰富的层次依赖关系并利用先验信息。最重要的是，它们通过后验分布实现了对不确定性的完整描述。实际中的后验计算通常通过MCMC进行，但对于包含大量观测的高维模型，这可能在计算上不可行。本文探讨了利用机器学习思想改进后验计算的潜力。通过规范化流、贝叶斯核心集、分布式贝叶斯推断和变分推断等案例，探索了具体未来方向。