64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Statistical inference for computer models

Organiser

DB
Dr Derek Bingham

Participants

  • DB
    Dr Derek Bingham
    (Chair)

  • DH
    Dave Higdon
    (Presenter/Speaker)
  • Hierarchical, non-stationary, spatial modeling to account for model error in computational hurricane models.

  • DW
    Daniel Williamson
    (Presenter/Speaker)
  • Real-time UQ for supporting policy makers in pandemics

  • LH
    Leanna House
    (Presenter/Speaker)
  • Population Sampling: One Approach for Modeling Uncertainty When Conditioning on Human Populations

  • NK
    Dr Natalie Klein
    (Presenter/Speaker)
  • Normalizing flows for flexible emulation of high-dimensional responses

  • CL
    Prof. Chunfang Devon Lin
    (Discussant)

  • Category: International Statistical Institute

    Abstract

    Computer models, or simulators, have become a commonplace way to study physical systems in areas such astronomy, climate, disease propagation, epidemiology, and engineering. In recent years, attention has turned to how to use the simulators in real-world situations, how to combine simulations with field data or other models to build predictive models, and how to quantify uncertainty in predictions. The need for new statistical methodology for inference using computational models is pressing. The session will be of broad interest because of the variety of applications and methods. Indeed, all talks are impactful insofar as they create new methods for real-world applications in epidemiology, climate, and hurricane tracking.