论文标题
催化先验：使用合成数据在贝叶斯分析中指定先验分布
Catalytic Priors: Using Synthetic Data to Specify Prior Distributions in Bayesian Analysis
论文作者
论文摘要
催化先前的分布提供了贝叶斯分析的先前分布的一般，易于使用和可解释的规格。当观察到的数据不足以稳定估计复杂的目标模型时，它们特别有益。通过使用从观察到的数据估计的更简单模型的预测分布来实现的合成数据来构建催化先验分布。我们使用劳动经济学的例子说明了催化先验方法的有用性。在示例中，由此产生的贝叶斯推论反映了观察到的数据的许多重要方面，并且基于催化先验的推断的估计准确性和预测性能优于或与其他常用的先前分布相比。我们进一步探讨了催化先验方法与一些流行的正则化方法之间的联系。我们期望催化先验方法在许多应用中有用。
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.
