Objective Bayesian Variable Selection
نویسندگان
چکیده
A novel fully automatic Bayesian procedure for variable selection in normal regression models is proposed, along with computational strategies for model posterior evaluation. A stochastic search algorithm is given, based on the Metropolis-Hastings Algorithm, that has a stationary distribution proportional to the model posterior probabilities. The procedure is illustrated on both simulated and real examples.
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