Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible nonlinear alternative to GLM while still providing better interpretability than machine learning techniques such as neural networks. In BGNLM, the methods of Bayesian Variable Selection and Model Averaging are applied in an extended GLM setting. Models are fitted to data using MCMC within a genetic framework by an algorithm called GMJMCMC. In this paper, we combine GMJMCMC with a novel algorithm called S-IRLS-SGD for estimating the marginal likelihoods in BGLM/BGNLM by subsampling from the data. This allows to apply GMJMCMC to tall data.

翻译：贝叶斯广义非线性模型（BGNLM）为广义线性模型（GLM）提供了灵活的非线性替代方案，同时仍比神经网络等机器学习技术具有更好的可解释性。在BGNLM中，贝叶斯变量选择与模型平均方法被应用于扩展的GLM框架。通过名为GMJMCMC的遗传算法框架内嵌马尔可夫链蒙特卡洛（MCMC）方法，模型得以拟合数据。本文提出将GMJMCMC与一种新型算法S-IRLS-SGD相结合，后者通过子采样数据来估计BGLM/BGNLM中的边际似然，从而实现GMJMCMC在"高维数据"场景下的应用。