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Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology

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  • Dehideniya, Mahasen B.
  • Drovandi, Christopher C.
  • McGree, James M.

Abstract

A methodology is proposed to derive Bayesian experimental designs for discriminating between rival epidemiological models with computationally intractable likelihoods. Methods from approximate Bayesian computation are used to facilitate inference in this setting, and an efficient implementation of this inference framework for approximating the expectation of utility functions is proposed. Three utility functions for model discrimination are considered, and the performance each utility is explored in designing experiments for discriminating between three epidemiological models; the death model, the Susceptible–Infected model, and the Susceptible–Exposed–Infected model. The challenge of efficiently locating optimal designs is addressed by an adaptation of the coordinate exchange algorithm which exploits parallel computational architectures.

Suggested Citation

  • Dehideniya, Mahasen B. & Drovandi, Christopher C. & McGree, James M., 2018. "Optimal Bayesian design for discriminating between models with intractable likelihoods in epidemiology," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 277-297.
    • Handle: RePEc:eee:csdana:v:124:y:2018:i:c:p:277-297
    DOI: 10.1016/j.csda.2018.03.004
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    References listed on IDEAS

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    1. Ryan, Elizabeth G. & Drovandi, Christopher C. & Thompson, M. Helen & Pettitt, Anthony N., 2014. "Towards Bayesian experimental design for nonlinear models that require a large number of sampling times," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 45-60.
    2. Simon Barthelmé & Nicolas Chopin, 2014. "Expectation Propagation for Likelihood-Free Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 315-333, March.
    3. Elizabeth G. Ryan & Christopher C. Drovandi & James M. McGree & Anthony N. Pettitt, 2016. "A Review of Modern Computational Algorithms for Bayesian Optimal Design," International Statistical Review, International Statistical Institute, vol. 84(1), pages 128-154, April.
    4. Xing Ju Lee & Christopher C. Drovandi & Anthony N. Pettitt, 2015. "Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets," Biometrics, The International Biometric Society, vol. 71(1), pages 198-207, March.
    5. Alex R. Cook & Gavin J. Gibson & Christopher A. Gilligan, 2008. "Optimal Observation Times in Experimental Epidemic Processes," Biometrics, The International Biometric Society, vol. 64(3), pages 860-868, September.
    6. Woods, David C. & McGree, James M. & Lewis, Susan M., 2017. "Model selection via Bayesian information capacity designs for generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 226-238.
    7. Simon N. Wood, 2010. "Statistical inference for noisy nonlinear ecological dynamic systems," Nature, Nature, vol. 466(7310), pages 1102-1104, August.
    8. Muller, Werner G & Ponce de Leon, Antonio C M, 1996. "Optimal Design of an Experiment in Economics," Economic Journal, Royal Economic Society, vol. 106(434), pages 122-127, January.
    9. McGree, J.M., 2017. "Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 207-225.
    10. Jonathan R. Stroud & Peter Müller & Gary L. Rosner, 2001. "Optimal sampling times in population pharmacokinetic studies," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 345-359.
    11. Christopher C. Drovandi & Anthony N. Pettitt, 2013. "Bayesian Experimental Design for Models with Intractable Likelihoods," Biometrics, The International Biometric Society, vol. 69(4), pages 937-948, December.
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