Bayesian Adaptive Sampling for Variable Selection and Model Averaging

摘要: For the problem of model choice in linear regression, we introduce a Bayesian adaptive sampling algorithm (BAS), that samples models without replacement from space models. problems permit enumeration all models, BAS is guaranteed to enumerate 2p iterations where p number potential variables under consideration. larger required, provide conditions which provides perfect replacement. When probabilities are marginal variable inclusion probabilities, may be viewed as “near” median probability Barbieri and Berger. As not known advance, discuss several strategies estimate adaptively within BAS. We illustrate performance using simulated real data show can outperform Markov chain Monte Carlo methods. The imple...