Journal article

On the efficient determination of optimal Bayesian experimental designs using ABC: A case study in optimal observation of epidemics

David J Price, Nigel G Bean, Joshua V Ross, Jonathan Tuke

JOURNAL OF STATISTICAL PLANNING AND INFERENCE | ELSEVIER SCIENCE BV | Published : 2016

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.

University of Melbourne Researchers

Grants

Awarded by Australian Research Council


Awarded by National Health and Medical Research Council (NHMRC Centre of Research Excellence PRISM2)


Awarded by ARC


Funding Acknowledgements

JVR acknowledges the support of the Australian Research Council (Future Fellowship, FT130100254) and the National Health and Medical Research Council (NHMRC Centre of Research Excellence PRISM<SUP>2</SUP>, APP1078068). The work of NGB and JVR is supported by ARC Discovery Project Funding (DP110101929). The authors wish to sincerely thank the reviewers for their insightful feedback and comments.