Catalytic prior distributions provide general, easy-to-use, and interpretable specifications of prior distributions for Bayesian analysis. They are particularly beneficial when the observed data are inadequate to stably estimate a complex target model. A catalytic prior distribution is constructed by augmenting the observed data with synthetic data that are sampled from the predictive distribution of a simpler model estimated from the observed data. We illustrate the usefulness of the catalytic prior approach using an example from labor economics. In the example, the resulting Bayesian inference reflects many important aspects of the observed data, and the estimation accuracy and predictive performance of the inference based on the catalytic prior are superior to, or comparable to, that of other commonly used prior distributions. We further explore the connection between the catalytic prior approach and a few popular regularization methods. We expect the catalytic prior approach to be useful in many applications.

翻译：催化先验分布为贝叶斯分析提供了一种通用、易用且可解释的先验分布指定方法。当观测数据不足以稳定估计复杂目标模型时，该方法尤为有效。催化先验分布的构建方法是通过整合观测数据与合成数据——这些合成数据来自基于观测数据估计的简单模型的预测分布。我们以劳动经济学案例为例，说明了催化先验方法的实用性。在该案例中，基于催化先验的贝叶斯推断能反映观测数据的诸多重要特征，且其估计精度与预测性能优于或等同于其他常用先验分布。我们进一步探讨了催化先验方法与几种流行正则化方法之间的关联。预计催化先验方法将在众多应用中发挥重要作用。