A Review of Modern Computational Algorithms for Bayesian Optimal Design

Elizabeth Gabrielle Ryan, Christopher Drovandi, James McGree, Anthony Pettitt

Research output: Contribution to journalArticlepeer-review

165 Citations (Scopus)
447 Downloads (Pure)


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.
Original languageEnglish
Number of pages27
Early online date15 Jun 2015
Publication statusE-pub ahead of print - 15 Jun 2015


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