Francisco Alejandro Díaz De la O (University of Liverpool)
Subset Simulation for Bayesian Updating and Model Selection
Hicks Lecture Theatre B, 2pm
On the one hand, the problems of model updating and model selection can be tackled using a Bayesian approach: the model parameters to be identified are treated as uncertain and the inference is done in terms of their posterior distribution. On the other hand, the engineering structural reliability problem can be solved by advanced Monte Carlo simulation techniques such as Subset Simulation. Recently, a formulation that connects the Bayesian updating problem and the structural reliability problem has been established. This opens up the possibility of efficient model calibration and model selection 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 and model selection without having to choose a multiplier in advance.