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On the Sensitivity of Bayes Factors to the Prior Distributions
Abstract
The Bayes factoris a Bayesian statistician's tool for model selection. Bayes factors can be highly sensitive to the prior distributions used for the parameters of the models under consideration. We discuss an approach for studying the sensitivity of the Bayes factor to the prior distributions for the parameters in the models being compared. The approach is found to be extremely useful for nested models; it has a graphical flavor making it more attractive than other common approaches to sensitivity analysis for Bayes factors.
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The BFs that PhyloAcc uses to determine which model best fits the data are known to be sensitive to within-model priors and are therefore somewhat controversial, even for very simple models.
“o ne potent ia l is s ue i s th at th e B Fs that PhyloAcc uses to determine which m ode l best fits the data are known to be sen sitiv e to within-m ode l priors ( Kass and Raftery 1995 ;Sinharay and Stern 2002 ), and are therefore s ome wha t con troversial ( Ten deiro an d Kiers 2019 ; va n Ravenzwaa ij a nd Wag enmakers 2022 ), ev en for v ery simple m ode ls.”
From Practical Guidance and Workflows for Identifying Fast Evolving Non-Coding Genomic Elements Using PhyloAcc (2024) · cites this paper as Sinharay and Stern 2002
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Thomas, G. W. C., Gemmell, P., Shakya, S. B., Hu, Z., Liu, J. S., Sackton, T. B., & Edwards, S. V. (2024) · Integrative And Comparative Biology · doi:10.1093/icb/icae056 · Cited by 5
Metadata
- Authors
- Sinharay, S., & Stern, H. S.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2002-08
- Volume
- 56
- Issue
- 3
- Pages
- 196-201
- ISSN
- 0003-1305
- 1537-2731
- Cited by
- 72
- References
- 0
- Language
- en
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