Speaker: 吴颖, 香港中文大学（深圳）

Inviter: 熊世峰

Title: Bayesian Inference for Stochastic Computer Simulations with A Random Fourier Features Method

Language: Chinese

Time & Venue: 2025.06.11 11:00-12:00 思源楼425

Abstract: Stochastic simulations are increasingly used to describe complex systems with uncertainties. To better characterize the uncertainty of such a simulation, we propose a random Fourier features method to fit fully Bayesian Gaussian process emulators for these simulations. The random Fourier features technique uses low-dimensional features of the correlation function of the Gaussian process to achieve a low-rank approximation of the correlation matrix. We prove the convergence of the proposed random Fourier features method. Simulation results show that the proposed method can significantly reduce computation time while maintaining high prediction accuracy. The advantage of the proposed method is also illustrated using a modern subway simulation.