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Activity Number: 477 - SPEED: Bayesian Methods and Applications in the Life and Social Sciences
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #329003 Presentation
Title: Calibrating a Stochastic Agent Based Model Using Quantile-Based Emulation
Author(s): Arindam Fadikar* and David Higdon
Companies: Virginia Tech and Virginia Tech
Keywords: uncertainty quantification; bayesian statistics; model validation; computer experiment; data assimilation; gaussian process

In a number of cases, the Quantile Gaussian Process (QGP) has proven effective in emulating stochastic, univariate computer model output (Plumlee and Tuo, 2014). In this paper, we develop an approach that uses this emulation approach within a Bayesian model calibration framework to calibrate an agent-based model of an epidemic. In addition, this approach is extended to handle the multivariate nature of the model output, which gives a time series of the count of infected individuals. The basic modeling approach is adapted from Higdon et al. (2008), using a basis representation to capture the multivariate model output. The approach is motivated with an example taken from the 2015 Ebola Challenge workshop which simulated an ebola epidemic to evaluate methodology.

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

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