Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 104 - Advances in Bayesian Analysis of Computer Models
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #323232
Title: Double Sequential Calibration Strategy for Stochastic Simulation Models
Author(s): Arindam Fadikar*
Companies: Argonne National Laboratory
Keywords: stochastic computer model; sequential filtering; uncertainty quantification

Sequential filtering approaches are common in estimating parameters for dynamical systems. In this work, we develop an approach that deals with sequential calibration of computer model of a dynamical system that evolves over time and space. As data from the system is observed at regular time intervals, inference on input parameters need to be updated periodically. In particular, we consider stochastic computer models where repeated runs of the simulation at same input yield a realization from an unknown distribution. The proposed double sequential calibration strategy employs two sequential learning schemes that learn the optimal values of the input parameters sequentially within each time interval given a limited simulation budget and then across time horizon as more and more data becomes available. The full posterior distribution of the input parameters is obtained at the end and uncertainty measures on the predictions are directly available from the calibrated simulations. An epidemic model that simulates infectious disease through contact network (e.g., Covid-19) will be used for illustration.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program