4 papers
Deep adaptive design with an evidential bias criterion
David Chen, Michael Evans, Xinwei Li +2
Bayesian optimal experimental design (BOED) aims to collect informative data by optimizing an expected utility reflecting the goals of an experiment. However, this optimization is…
Physics Informed Multi-task Joint Generative Learning for Arterial Vehicle Trajectory Reconstruction Considering Lane Changing Behavior
Mengyun Xu, Jie Fang, Eui-Jin Kim +2
Reconstructing complete traffic flow time-space diagrams from vehicle trajectories offer a comprehensive view on traffic dynamics at arterial intersections. However, obtaining full…
A Large Language Model for Feasible and Diverse Population Synthesis
Sung Yoo Lim, Hyunsoo Yun, Prateek Bansal +2
Generating a synthetic population that is both feasible and diverse is crucial for ensuring the validity of downstream activity schedule simulation in activity-based models (ABMs).…
Multi-objective Bayesian optimization for Likelihood-Free inference in sequential sampling models of decision making
David Chen, Xinwei Li, Eui-Jin Kim +2
Statistical models are often defined by a generative process for simulating synthetic data, but this can lead to intractable likelihoods. Likelihood free inference (LFI) methods en…