Bayesian Model Averaging
نویسنده
چکیده
Bayesian Model averaging is a natural response to model uncertainty. It has become an important practical tool for dealing with model uncertainty, in particular in empirical settings with large numbers of potential models and relatively limited numbers of observations. Most of this paper focuses on the problem of variable selection in normal linear regression models, but also briefly considers a more general setting. The article surveys a number of prior structures proposed in the literature, and presents in some detail the most commonly used prior setup. Model selection consistency and practical implementation are also briefly discussed.
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