Population SAMC vs SAMC: Convergence and Applications to Gene Selection Problems

@inproceedings{Wu2013PopulationSV,
  title={Population SAMC vs SAMC: Convergence and Applications to Gene Selection Problems},
  author={Mingqi Wu and Faming Liang},
  year={2013}
}
The Bayesian model selection approach has been adopted by more and more people when analyzing a large data. However, it is known that the reversible jump MCMC (RJMCMC) algorithm, which is perhaps the most popular MCMC algorithm for Bayesian model selection, is prone to get trapped into local modes when the model space is complex. The stochastic approximation Monte Carlo (SAMC) algorithm essentially overcomes the local trap problem suffered by conventional MCMC algorithms by introducing a self… CONTINUE READING

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