Adaptive Markov Chain Monte Carlo for Bayesian Variable Selection

@inproceedings{Ji2008AdaptiveMC,
  title={Adaptive Markov Chain Monte Carlo for Bayesian Variable Selection},
  author={C. Ji and Scott C. Schmidler},
  year={2008}
}
We describe adaptive Markov chain Monte Carlo (MCMC) methods for sampling posterior distributions arising from Bayesian variable selection problems. Point mass mixture priors are commonly used in Bayesian variable selection problems in regression. However, for generalized linear and nonlinear models where the conditional densities cannot be obtained directly, the resulting mixture posterior may be difficult to sample using standard MCMC methods due to multimodality. We introduce an adaptive… CONTINUE READING

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