In this manuscript we introduce Group Spike-and-slab Variational Bayes (GSVB), a scalable method for group sparse regression. We constrcut a fast co-ordinate ascent variational inference algorithm for several model families including: the Gaussian, Binomail and Poisson. Through extensive numerical studies we demonstrate that GSVB provides state-of-the-art performance, offering a computationally inexpensive substitute to MCMC, whilst performing comparably or better than existing MAP methods. Additionally, we analyze three real world datasets wherein we highlight the practical utility of our method, demonstrating that GSVB provides parsimonious models with excellent predictive performance, variable selection and uncertainty quantification.

翻译：本文引入分组尖峰与板状变分贝叶斯（GSVB）方法，一种用于分组稀疏回归的可扩展方法。我们针对包括高斯分布、二项分布和泊松分布在内的多个模型族，构建了快速的坐标上升变分推理算法。通过大量数值研究，我们证明GSVB能够提供最先进的性能，作为马尔可夫链蒙特卡洛方法（MCMC）的计算高效替代方案，同时与现有最大后验概率（MAP）方法相比表现相当或更优。此外，我们分析了三个真实世界数据集，突显了该方法在实际应用中的效用，表明GSVB能够提供简洁的模型，具有出色的预测性能、变量选择和不确定性量化能力。