This study conducts a comprehensive review of the promises and challenges of implementing Bayesian statistics in natural hazards engineering. The reviewed natural hazards include earthquakes, floods, extreme wind events, wildfires, and landslides and debris flows. An attributes matrix is developed under each hazard to analyze each study based on its associated scale of analysis, topic area, Bayesian method, and data resource. In particular, the state-of-the-art survey elaborates the level of involvement for three categories of Bayesian methods, such as Bayesian model updating, Bayesian network, and Bayesian neural network, in the topic areas of hazard analysis, risk assessment, and structural health monitoring. In general, the existing research in natural hazards engineering is benefited by leveraging Bayesian statistics to handle uncertainties explicitly and deal with large-scale problems that involve different types of data inputs. However, the substantial computational cost and the determination of prior probability distributions are two major challenges bottlenecking the future development of Bayesian statistics. Compared with machine learning, Bayesian approaches offer more transparent model inference and exhibit different abilities to avoid data over fitting. This reviewed work can serve as a sound reference for interested practitioners and researchers to practice, develop, and promote broader and more in-depth Bayesian advances in solving grand challenges in natural hazards engineering.

这项研究对在自然灾害工程中实施贝叶斯统计的前景和挑战进行了全面回顾。经过审查的自然灾害包括地震，洪水，极端风灾，野火以及滑坡和泥石流。在每种危害下建立一个属性矩阵，以便根据与研究相关的分析范围，主题领域，贝叶斯方法和数据资源对每个研究进行分析。尤其是，最新的调查详细说明了危害分析，风险评估和风险管理等主题领域中三类贝叶斯方法（例如贝叶斯模型更新，贝叶斯网络和贝叶斯神经网络）的参与程度。结构健康监测。一般来说，现有的自然灾害工程研究得益于利用贝叶斯统计数据来明确处理不确定性并处理涉及不同类型数据输入的大规模问题。然而，巨大的计算成本和确定先验概率分布是制约贝叶斯统计未来发展的两个主要挑战。与机器学习相比，贝叶斯方法提供了更透明的模型推断，并展现出不同的能力来避免数据过度拟合。这项经过审查的工作可以为感兴趣的从业人员和研究人员在解决自然灾害工程中的巨大挑战方面实践，发展和促进更广泛，更深入的贝叶斯方面的发展提供有益的参考。