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May, 2015
高维贝叶斯变量选择的计算复杂度
On the Computational Complexity of High-Dimensional Bayesian Variable Selection
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Yun Yang, Martin J. Wainwright, Michael I. Jordan
TL;DR
本文研究高维贝叶斯线性回归的计算复杂度，介绍了一种截尾稀疏先验变量选择方法，通过Metropolis-Hastings算法，保证了变量选择的一致性和快速混合。
Abstract
We study the computational complexity of
markov chain monte carlo
(MCMC) methods for high-dimensional
bayesian linear regression
under sparsity constraints. We first show that a Bayesian approach can achieve vari
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