Journal of Biometrics & Biostatistics

Mingqi Wu

Publications

• Research Article
  Population SAMC vs SAMC: Convergence and Applications to Gene Selection Problems
  Author(s): Mingqi Wu and Faming LiangMingqi Wu and Faming Liang
  
  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-adjusting mechanism based on the past samples. In this paper, we propose a population SAMC (Pop-SAMC) algorithm, which works on a population of SAMC chains and can make use of crossover operators from genetic algorithms to further improve its efficiency. Under mild conditions, we show the convergence of this algorithm. Comparing to the single chain SAMC algorithm, Pop-SAMC provides a more efficie.. Read More»
  DOI: 10.4172/2155-6180.S1-002

  Abstract PDF