Calibrating a Stochastic Agent Based Model Using Quantile-based Emulation

Autor:	Fadikar, Arindam, Higdon, Dave, Chen, Jiangzhuo, Lewis, Brian, Venkatramanan, Srini, Marathe, Madhav
Rok vydání:	2017
Předmět:	Statistics - Applications Statistics - Computation 62P47 62G43 62H46
Druh dokumentu:	Working Paper
DOI:	10.1137/17M1161233
Popis:	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. Comment: 20 pages, 12 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.00546 Zobrazit plný text záznamu View this record from Arxiv