We describe an adaptive Markov chain Monte Carlo method suitable for the estimation of rare failure probabilities in complex probabilistic models. This method, the Accelerated Weight Histogram (AWH) method, has its origin in statistical physics (Lidmar, 2012) and has successfully been applied to molecular dynamics simulations in biophysics. Here we introduce it in the context of structural reliability and demonstrate its usefulness for calculation of failure probabilities in some selected problems of varying degrees of complexity and compare with other established techniques, e.g., subset simulations.
翻译:我们描述一种适应性Markov链 Monte Carlo 方法,适合于估计复杂概率模型中罕见的失灵概率,这种方法,即加速重量直方图(AWH)方法,起源于统计物理(Lidmar,2012年),并成功应用于生物物理中的分子动态模拟,我们在此介绍该方法的结构可靠性,并表明该方法可用于计算某些复杂程度不同的选定问题的失灵概率,并与其它既定技术,例如子集模拟进行比较。