Parametric emulation and inference in computationally expensive integrated urban water quality simulators

Accelerating Bayesian Inference in Computationally Expensive Computer Models Using Local and Global Approximations

Computational models of complex phenomena are an important resource for scientists and engineers. However, many state-of-the-art simulations of physical systems are computationally expensive to evaluate and are black box—meaning that they can be run, but their internal workings cannot be inspected or changed. Directly applying uncertainty quantification algorithms, such as those for forward unc...

Emulation of random output simulators

Bayesian Nonparametric and Parametric Inference

This paper reviews Bayesian Nonparametric methods and discusses how parametric predictive densities can be constructed using nonparametric ideas.

Regarding probabilistic analysis and computationally expensive models: necessary and required?

OBJECTIVE To assess the importance of considering decision uncertainty, the appropriateness of probabilistic sensitivity analysis (PSA), and the use of patient-level simulation (PLS) in appraisals for the National Institute for Health and Clinical Excellence (NICE). METHODS Decision-makers require estimates of decision uncertainty alongside expected net benefits (NB) of interventions. This re...

Design and Optimization of Computationally Expensive Engineering Systems

Engineering systems form the basis on which our day-to-day lives depend on. In many cases, designers are interested in identifying an optimal design of an engineering system. However, very often, the process of engineering design optimization is complex and involves time-expensive simulations or the need to satisfy not just one, but multiple objectives. This thesis aims to explore the area of e...