A review of modern computational algorithms for Bayesian optimal design
Ryan, Elizabeth G., Drovandi, Christopher C., McGree, James M., & Pettitt, Anthony N. (2016) A review of modern computational algorithms for Bayesian optimal design. International Statistical Review, 84(1), pp. 128-154.
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Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design.
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|Item Type:||Journal Article|
|Keywords:||Bayesian optimal design, Decision theory, Utility function, Stochastic optimisation, Posterior distribution approximation|
|Divisions:||Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 The Authors. International Statistical Review Copyright 2015 International Statistical Institute|
|Deposited On:||12 Aug 2014 01:30|
|Last Modified:||16 May 2016 03:27|
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