Bayesian variable selection is an important method for discovering variables which are most useful for explaining the variation in a response. The widespread use of this method has been restricted by the challenging computational problem of sampling from the corresponding posterior distribution. Recently, the use of adaptive Monte Carlo methods has been shown to lead to performance improvement over traditionally used algorithms in linear regression models. This paper looks at applying one of these algorithms (the adaptively scaled independence sampler) to logistic regression and accelerated failure time models. We investigate the use of this algorithm with data augmentation, Laplace approximation and the correlated pseudo-marginal method. The performance of the algorithms is compared on several genomic data sets.

贝叶斯变量选择是发现变量的重要方法，这些变量对于解释响应的变化最有用。该方法的广泛使用受到从相应的后验分布采样的具有挑战性的计算问题的限制。近来，已经证明，与线性回归模型中传统使用的算法相比，自适应蒙特卡洛方法的使用可导致性能提高。本文着眼于将这些算法之一（自适应缩放的独立采样器）应用于逻辑回归和加速故障时间模型。我们研究了该算法与数据扩充，拉普拉斯逼近和相关伪边际方法的结合使用。在几个基因组数据集上比较了算法的性能。