Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models
نویسندگان
چکیده
This article describes a method for efficient posterior simulation for Bayesian variable selection in Generalized Linear Models with many regressors but few observations. A proposal on model space is described which contains a tuneable parameter. An adaptive approach to choosing this tuning parameter is described which allows automatic, efficient computation in these models. The method is applied to examples from normal linear and probit regression.
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