New Monte Carlo method is computationally more effective for quantifying uncertainty
(PHYSorg.com Mathematics)

Uncertainty quantification (UQ) is a statistical technique to predict many complex phenomena such as weather conditions and tsunami risks. It involves the combination of real-life data (e.g. weather measurements) together with mathematical equations to model physical systems that are well-understood. These complex models are usually associated with either high-dimensional objects, large datasets or possibly both. In such scenarios, it is important that the required computational methodology to estimate such models is resource-efficient. Prof Ajay JASRA from the Department of Statistics and Applied Probability, NUS and his collaborators have proposed a more efficient approach to perform UQ calculations.