Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/99437
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Type: | Journal article |
Title: | On the efficient determination of optimal Bayesian experimental designs using ABC: a case study in optimal observation of epidemics |
Author: | Price, D.J. Bean, N.G. Ross, J.V. Tuke, J. |
Citation: | Journal of Statistical Planning and Inference, 2016; 172:1-15 |
Publisher: | Elsevier |
Issue Date: | 2016 |
ISSN: | 0378-3758 1873-1171 |
Statement of Responsibility: | David J. Price, Nigel G. Bean, Joshua V. Ross, Jonathan Tuke |
Abstract: | We present a new method for determining optimal Bayesian experimental designs, which we refer to as ABCdE. ABCdE uses Approximate Bayesian Computation to calculate the utility of possible designs. For problems with a low-dimensional design space, it evaluates the designs’ utility in less computation time compared to existing methods. We apply ABCdE to stochastic epidemic models. Optimal designs evaluated using ABCdE are compared to those evaluated using existing methods for the stochastic death and susceptible–infectious (SI) models. We present the Bayesian optimal experimental designs for the susceptible–infectious–susceptible (SIS) model using ABCdE. |
Keywords: | Bayesian optimal design; stochastic epidemic models; Approximate Bayesian Computation |
Rights: | © 2015 Elsevier B.V. All rights reserved. |
DOI: | 10.1016/j.jspi.2015.12.008 |
Grant ID: | http://purl.org/au-research/grants/arc/FT130100254 http://purl.org/au-research/grants/nhmrc/1078068 http://purl.org/au-research/grants/arc/DP110101929 |
Published version: | http://dx.doi.org/10.1016/j.jspi.2015.12.008 |
Appears in Collections: | Aurora harvest 7 Mathematical Sciences publications |
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hdl_99437.pdf | Accepted version | 573.64 kB | Adobe PDF | View/Open |
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