A Shrinkage-Thresholding Metropolis Adjusted Langevin Algorithm for Bayesian Variable Selection.
IEEE Journal of Selected Topics in Signal Processing(2016)
摘要
This paper introduces a new Markov Chain Monte Carlo method for Bayesian variable selection in high dimensional settings. The algorithm is a Hastings-Metropolis sampler with a proposal mechanism which combines a Metropolis Adjusted Langevin (MALA) step to propose local moves associated with a shrinkage-thresholding step allowing to propose new models. The geometric ergodicity of this new trans-dim...
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关键词
Signal processing algorithms,Input variables,Algorithm design and analysis,Bayes methods,Monte Carlo methods,Proposals
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