Scalable Bayesian inference for the generalized linear mixed model

arXiv:2403.03007v1 Announce Type: cross
Abstract: The generalized linear mixed model (GLMM) is a popular statistical approach for handling correlated data, and is used extensively in applications areas where big data is common, including biomedical data settings. The focus of this paper is scalable statistical inference for the GLMM, where we define statistical inference as: (i) estimation of population parameters, and (ii) evaluation of scientific hypotheses in the presence of uncertainty. Artificial intelligence (AI) learning algorithms excel at scalable statistical estimation, …

abstract applications arxiv bayesian bayesian inference big big data biomedical data focus generalized glmm inference linear mixed paper popular scalable stat.co statistical stat.me stat.ml type