Adaptive Markov Chain Monte Carlo for Bayesian Variable Selection

  title={Adaptive Markov Chain Monte Carlo for Bayesian Variable Selection},
  author={C. Ji and Scott C. Schmidler},
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


Publications citing this paper.
Showing 1-10 of 10 extracted citations

Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics • 2018
View 1 Excerpt


Publications referenced by this paper.
Showing 1-10 of 39 references

Nonparametric Bayesian Kernel Models

View 13 Excerpts
Highly Influenced

Stability of Stochastic Approximation under Verifiable Conditions

Proceedings of the 44th IEEE Conference on Decision and Control • 2005
View 10 Excerpts
Highly Influenced

An adaptive Metropolis algorithm

View 8 Excerpts
Highly Influenced

Bayesian variable selection in linear regression (with discussion)

T. J. Mitchell, J. J. Beauchamp
J. Amer. Statist. Assoc., 83:1023–1036. • 1988
View 5 Excerpts
Highly Influenced

Theory of Helix-Coil Transitions in Biopolymers: Statistical Mechanical Theory of Order-Disorder Transitions in Biological Macromolecules

D. Poland, H. A. Scheraga
Academic Press. • 1970
View 3 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…