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Bayesian Model Selection and Model Averaging
1Carnegie Mellon University
Journal of Mathematical Psychology
|March 29, 2000
Summary
This review covers objective Bayesian methods for model selection and averaging using noninformative priors. It details implementation, approximations, and comparisons to other statistical approaches.
Area of Science:
- Statistics
- Statistical Modeling
Background:
- Model selection and averaging are critical in statistical analysis.
- Bayesian approaches offer a coherent framework for these tasks.
Purpose of the Study:
- To review the Bayesian approach to model selection and model averaging.
- To emphasize objective Bayesian methods utilizing noninformative priors.
Main Methods:
- Review of Bayesian methodologies.
- Focus on noninformative prior distributions.
- Discussion of implementation and approximation techniques.
Main Results:
- Objective Bayesian methods provide a principled framework for model selection and averaging.
- Noninformative priors offer a consistent approach when prior knowledge is limited.
- The paper outlines practical implementation and computational considerations.
Conclusions:
- Bayesian model selection and averaging, particularly with objective methods, are powerful tools.
- Understanding implementation details and approximations is key for practical application.
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