The behaviour of systems is determined by various parameters. Due to several reasons like e. g. manufacturing tolerances these parameters can have some uncertainties. Corner Case and Monte Carlo simulations are well known approaches to handle uncertain systems. They sample the corners and random points of the parameter space, respectively. Both require many runs and do not guarantee the inclusi...

The problem of reliability analysis of randomly driven, stochastically parametered, vibrating structures is considered. The excitation components are assumed to be jointly stationary, Gaussian random processes. The randomness in structural parameters is modelled through a vector of mutually correlated, non-Gaussian random variables. The structure is defined to be safe if a set of response quant...

To account for the input-model and input-parameter uncertainties inherent in many simulations as well as the usual stochastic uncertainty, we present a Bayesian input-modeling technique that yields improved point and confidence-interval estimators for a selected posterior mean response. Exploiting prior information to specify the prior probabilities of the postulated input models and the associ...

The development of new geoacoustic inversion procedures for use into the kHz frequency regime, the development of methods for estimating the entire posteriori probability densities of the geoacoustic parameters being investigated along with the mapping of these parameter uncertainties through to characterizations of applied interest (e.g. transmission loss), and the demonstration of their use i...

The rational management of water resource systems requires an understanding of their response to existing and planned schemes of exploitation, pollution prevention and/or remediation. Such understanding requires the collection of data to help characterize the system and monitor its response to existing and future stresses. It also requires incorporating such data in models of system makeup, wat...