This paper introduces a sequential multiple importance sampling (SeMIS) algorithm for high-dimensional Bayesian inference. The method estimates Bayesian evidence using all generated samples from each proposal distribution while obtaining posterior samples through an importance-resampling scheme. A key innovation of SeMIS is the use of a softly truncated prior distribution as the intermediate proposal, providing a new way bridging prior and posterior distributions. By enabling samples from high-likelihood regions to traverse low-probability zones, SeMIS enhances mode mixing in challenging inference problems. Comparative evaluations against subset simulation (SuS) and adaptive Bayesian updating with structural reliability methods (aBUS) demonstrate that SeMIS achieves superior performance in evidence estimation (lower bias and variance) and posterior sampling (higher effective sample sizes and closer approximation to the true posterior), particularly for multimodal distributions. The efficacy of SeMIS is further validated in a high-dimensional finite element model updating application, where it successfully localizes structural damages by quantifying stiffness loss. The proposed algorithm not only advances Bayesian computation for complex posterior distributions but also provides a robust tool for uncertainty quantification in civil engineering systems, offering new possibilities for probabilistic structural health monitoring.

翻译：本文提出了一种用于高维贝叶斯推断的序贯多重重要性采样算法。该方法利用每个提议分布生成的所有样本来估计贝叶斯证据，同时通过重要性重采样方案获取后验样本。SeMIS的一个关键创新是采用软截断先验分布作为中间提议分布，为连接先验与后验分布提供了新途径。通过使来自高似然区域的样本能够穿越低概率区域，SeMIS增强了具有挑战性的推断问题中的模态混合能力。与子集模拟及基于结构可靠度方法的自适应贝叶斯更新算法的对比评估表明，SeMIS在证据估计和後验采样方面均表现出更优越的性能，特别是在多峰分布场景中。SeMIS的效能在一个高维有限元模型更新应用中得到了进一步验证，成功通过量化刚度损失实现了结构损伤定位。该算法不仅推动了复杂后验分布的贝叶斯计算发展，还为土木工程系统的不确定性量化提供了鲁棒工具，为概率结构健康监测开辟了新的可能性。