ABSTRACT While public debate over gun control in the United States has often hinged on individual public mass shooting incidents, legislative action should be informed by knowledge of the long-term evolution of these events. We present a new Bayesian model for the annualized rate of public mass shootings in the United States based on a Gaussian process with a time-varying mean function. While we present specific findings on long- and short-term trends of these shootings in the U.S., our focus is on understanding the role of model design and prior information in policy analysis. Using a Markov chain Monte Carlo inference technique, we explore the posterior consequences of different prior choices and explore correlations between hyperparameters. We demonstrate that the findings about the long-term evolution of the annualized rate of public mass shootings are robust to choices about prior information, while inferences about the timescale and amplitude of short-term variation depend sensitively on the prior. This work addresses the policy implications of implicit and explicit choices of prior information in model design and the utility of full Bayesian inference in evaluating the consequences of those choices.

中文翻译：

---

先验信息在推断美国大规模枪击案年率中的作用

摘要虽然在美国，关于枪支管制的公开辩论通常取决于个别的公共大规模枪击事件，但立法行动应基于对这些事件的长期演变的了解。我们基于具有时变均值函数的高斯过程，提出了一种新的贝叶斯模型，用于在美国进行公共群众枪击的年率。在介绍美国枪击事件的长期和短期趋势的具体发现时，我们的重点是了解模型设计和先验信息在政策分析中的作用。使用马尔可夫链蒙特卡罗推理技术，我们探索了不同先验选择的后验结果，并探索了超参数之间的相关性。我们证明，有关公共群众枪击案年率的长期演变的发现对先验信息的选择是有力的，而关于短期变化的时标和幅度的推论则敏感地取决于先验信息。这项工作解决了模型设计中隐式和显式选择先验信息的政策含义，以及全面贝叶斯推理在评估这些选择的后果时的效用。