A Review of Modern Computational Algorithms for Bayesian Optimal Design
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...
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| Published in: | International statistical review Vol. 84; no. 1; pp. 128 - 154 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Hoboken
Blackwell Publishing Ltd
01.04.2016
Blackwell Publishing John Wiley & Sons, Inc |
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| ISSN: | 0306-7734, 1751-5823 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. Summary 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. |
| Author | McGree, James M. Pettitt, Anthony N. Drovandi, Christopher C. Ryan, Elizabeth G. |
| Author_xml | – sequence: 1 givenname: Elizabeth G. surname: Ryan fullname: Ryan, Elizabeth G. email: elizabeth.ryan@kcl.ac.uk organization: School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia – sequence: 2 givenname: Christopher C. surname: Drovandi fullname: Drovandi, Christopher C. organization: School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia – sequence: 3 givenname: James M. surname: McGree fullname: McGree, James M. organization: School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia – sequence: 4 givenname: Anthony N. surname: Pettitt fullname: Pettitt, Anthony N. organization: School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia |
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| Snippet | 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... Summary Bayesian experimental design is a fast growing area of research with many real‐world applications. As computational power has increased over the years,... 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... |
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| SubjectTerms | Algorithms Bayesian analysis Bayesian optimal design Computation Computer simulation decision theory Experiments Mathematical analysis Mathematical models Monte Carlo simulation Optimization posterior distribution approximation Simulation stochastic optimisation Utilities utility function |
| Title | A Review of Modern Computational Algorithms for Bayesian Optimal Design |
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