Calibrating a Stochastic Agent Based Model Using Quantile-based Emulation
arXiv:1712.00546 · doi:10.1137/17M1161233
Abstract
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.
20 pages, 12 figures
References in corpus (4)
- Inferring population history with DIYABC: a user-friendly approach to Approximate Bayesian Computation
- Computer model validation with functional output
- The Coyote Universe III: Simulation Suite and Precision Emulator for the Nonlinear Matter Power Spectrum
- Parameter estimation for computationally intensive nonlinear regression with an application to climate modeling