Bayes factors: Prior sensitivity and model generalizability

DOI10.1016/j.jmp.2008.03.002zbMath1152.91771OpenAlexW2164167147MaRDI QIDQ2519503

Charles C. Liu, Murray Aitkin

Publication date: 26 January 2009

Published in: Journal of Mathematical Psychology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmp.2008.03.002

zbMATH Keywords

cross-validationBayesian information criterionminimum description lengthnon-informative priorJeffreys-Lindley paradoxforgetting function

Mathematics Subject Classification ID

Mathematical psychology (91E99)

Related Items (19)

Nonlinear sparse Bayesian learning for physics-based models ⋮ The philosophy of Bayes factors and the quantification of statistical evidence ⋮ Harold Jeffreys's default Bayes factor hypothesis tests: explanation, extension, and application in psychology ⋮ Adjusted priors for Bayes factors involving reparameterized order constraints ⋮ Bayesian model selection for nonlinear aeroelastic systems using wind-tunnel data ⋮ Standard errors, priors, and bridge sampling: a discussion of Liu et al. ⋮ Prior sensitivity in theory testing: an apologia for the Bayes factor ⋮ Reflections on statistical modelling: A conversation with Murray Aitkin ⋮ Blending Bayesian and frequentist methods according to the precision of prior information with applications to hypothesis testing ⋮ The flexibility of models of recognition memory: the case of confidence ratings ⋮ Selecting amongst multinomial models: an apologia for normalized maximum likelihood ⋮ The form of the forgetting curve and the fate of memories ⋮ Hierarchical models of simple mechanisms underlying confidence in decision making ⋮ A tutorial on approximate Bayesian computation ⋮ The flexibility of models of recognition memory: an analysis by the minimum-description length principle ⋮ Distribution-free tests of stochastic dominance for small samples ⋮ Default Bayes factors for ANOVA designs ⋮ Thermodynamic integration and steppingstone sampling methods for estimating Bayes factors: a tutorial ⋮ Evaluating predictors of dispersion: a comparison of dominance analysis and Bayesian model averaging

Uses Software

• R
• R2WinBUGS
• BaM
• BayesDA

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