Specification of prior distributions under model uncertainty
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Summary
A particular joint specification of the prior distribution across models is proposed so that sensitivity of posterior model probabilities to the dispersion of prior distributions for the parameters of individual models (Lindley's paradox) is diminished.
- Type
- article
- Published
- 2009-05-13
- Cited by
- 0
- References
- 47
- Access
- Open access
- OpenAlex
- https://openalex.org/W57089245
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:16729412
Keywords
Bayesian linear regression, Posterior predictive distribution, Prior probability, Bayesian probability, Mathematics
References
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- Highly Structured Stochastic Systems
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- Bayesian Mode Regression
- A Predictive Approach to Model Selection
- A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz Criterion
- The risk inflation criterion for multiple regression
- Bayesian variable selection with related predictors
- Bayesian Information Criterion for Censored Survival Models
- Bayesian variable and link determination for generalised linear models
- Shotgun Stochastic Search for “Large p” Regression
- Theory of Statistics
- Mixtures of g Priors for Bayesian Variable Selection
- Predictive specification of prior model probabilities in variable selection
- An alternative to the standard Bayesian procedure for discrimination between normal linear models
- Incorporating prior information into the analysis of contingency tables.
- Theory of Probability (3rd Edition)
- On the posterior odds of time series models
- Posterior odds ratios for selected regression hypotheses
- Misinformation in the conjugate prior for the linear model with implications for free-knot spline modelling.
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