We define a novel class of additive models called Extended Latent Gaussian Models and develop a fast, scalable approximate Bayesian inference methodology for this class. The new class covers a wide range of interesting models, and the new methodology is better suited to large samples than existing approaches. We discuss convergence theory for our posterior approximations. We then illustrate the computational aspects of our approach through a comparison to existing methods, and demonstrate its application in three challenging examples: the analysis of aggregated spatial point process data, the fitting of a Cox proportional hazards model with partial likelihood and a latent spatial point process, and an astrophysical model for estimating the mass of the Milky Way in the presence of multivariate measurement uncertainties. Computations make use of the publicly available aghq package in the R language and code for the examples in the paper is available from https://github.com/awstringer1/elgm-paper-code
翻译:我们定义了新型的添加模型类别,称为扩展的Libent Gaussian模型,并为这一类制定了一种快速、可缩放的近似贝叶斯推算方法。新类别涵盖广泛的有趣模型,新方法比现有方法更适合大样本。我们讨论了后方近似的趋同理论。然后我们通过比较现有方法来说明我们方法的计算方面,并用三个具有挑战性的例子来说明其应用:分析综合空间点处理数据、将考克斯成比例危害模型与局部可能性和潜在空间点过程相匹配,以及在存在多变量测量不确定性的情况下估算银河质量的天体物理模型。计算利用以R语言提供的可公开提供的aghq软件包,本文示例的代码见https://github.com/awstringer1/elgm-paper-code。