Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design

McGree, James M. (2017) Developments of the total entropy utility function for the dual purpose of model discrimination and parameter estimation in Bayesian design. Computational Statistics and Data Analysis, 113, pp. 207-225.

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The total entropy utility function is considered for the dual purpose of Bayesian design for model discrimination and parameter estimation. A sequential design setting is proposed where it is shown how to efficiently estimate the total entropy utility for a wide variety of data types. Utility estimation relies on forming particle approximations to a number of intractable integrals which is afforded by the use of the sequential Monte Carlo algorithm for Bayesian inference. A number of motivating examples are considered for demonstrating the performance of total entropy in comparison to utilities for model discrimination and parameter estimation. The results suggest that the total entropy utility selects designs which are efficient under both experimental goals with little compromise in achieving either goal. As such, the total entropy utility is advocated as a general utility for Bayesian design in the presence of model uncertainty.

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ID Code: 86673
Item Type: Journal Article
Refereed: Yes
Additional URLs:
Keywords: generalized linear models, optimal design, particle filter, sequential design, sequential Monte Carlo, total entropy, generalized nonlinear models
DOI: 10.1016/j.csda.2016.05.020
ISSN: 0167-9473
Divisions: Current > Institutes > Institute for Future Environments
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: 2016 Elsevier B.V.
Copyright Statement: This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Deposited On: 20 Aug 2015 22:22
Last Modified: 17 Jul 2017 23:01

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