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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Veröffentlicht in:International statistical review Jg. 84; H. 1; S. 128 - 154
Hauptverfasser: Ryan, Elizabeth G., Drovandi, Christopher C., McGree, James M., Pettitt, Anthony N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Hoboken Blackwell Publishing Ltd 01.04.2016
Blackwell Publishing
John Wiley & Sons, Inc
Schlagworte:
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
ISSN:0306-7734, 1751-5823
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Zusammenfassung: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.
Bibliographie:istex:34C1AA96AEBFE295756F9E7B8D3A1FD76794CD07
ArticleID:INSR12107
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ISSN:0306-7734
1751-5823
DOI:10.1111/insr.12107