Connections between Rare-Event Simulation and Bayesian Inference On the one hand, Bayesian inference is concerned with updating model parameters given data by computing their posterior distribution. On the other hand, rare-event simulation involves Monte Carlo strategies such as Subset Simulation. Recently, a formulation that connects the Bayesian updating problem and rare-event simulation has been established. This opens up the possibility of efficient model updating using Subset Simulation. The formulation, called BUS (Bayesian Updating with Structural reliability methods), is based on a rejection principle. Its theoretical correctness and efficiency requires the prudent choice of a multiplier, which has remained an open question. Motivated by this problem, this talk presents a study of BUS. The discussion will lead to a revised formulation that allows Subset Simulation to be used for Bayesian updating without having to choose a multiplier in advance.