Approximate bayesian computation for finite element model updating

Abstract

In recent years, there has been a growing interest in Bayesian model updating methods. The learning process is characterised by estimating the probability distribution of a random parameter within an ensemble of data and prior information. A crucial component of these methods is a marginal likelihood term. However, for most models, an analytical expression cannot be found or can be computationally intractable. A possible solution is to perform likelihood-free inference. Recently, there has been a development of techniques known as Approximate Bayesian Computation (ABC) methods. This work explores the coupling between finite element model updating and ABC, its potential and its limitations.