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Computational methods for Bayesian model choice

Robert, Christian P.; Wraith, Darren (2009), Computational methods for Bayesian model choice, in Goggans, Paul M.; Chang, Chung-Yong, Bayesian Inference and Maximum Entropy Methods in Science and Engineering: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, AIP Publishing, p. 12. http://dx.doi.org/10.1063/1.3275622

Type
Communication / Conférence
External document link
http://arxiv.org/abs/0907.5123v1
Date
2009
Conference title
Bayesian Inference and Maximum Entropy Methods in Science and Engineering: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Conference date
2009-07
Conference city
Oxford (Mississippi)
Conference country
États-Unis
Book title
Bayesian Inference and Maximum Entropy Methods in Science and Engineering: The 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering
Book author
Goggans, Paul M.; Chang, Chung-Yong
Publisher
AIP Publishing
Series title
AIP Conference Proceedings
Series number
1193
Pages
12
Publication identifier
http://dx.doi.org/10.1063/1.3275622
Metadata
Show full item record
Author(s)
Robert, Christian P.
Wraith, Darren
Abstract (EN)
In this note, we shortly survey some recent approaches on the approximation of the Bayes factor used in Bayesian hypothesis testing and in Bayesian model choice. In particular, we reassess importance sampling, harmonic mean sampling, and nested sampling from a unified perspective.
Subjects / Keywords
Bayesian model

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