Fast Methods for Posterior Inference of Two-Group Normal-Normal Models.

ABSTRACT: We describe a class of algorithms for evaluating posterior moments of certain Bayesian linear regression models with a normal likelihood and a normal prior on the regression coefficients. The proposed methods can be used for hierarchical mixed effects models with partial pooling over one group of predictors, as well as random effects models with partial pooling over two groups of predictors. We demonstrate the performance of the methods on two applications, one involving U.S. opinion polls and one involving the modeling of COVID-19 outbreaks in Israel using survey data. The algorithms involve analytical marginalization of regression coefficients followed by numerical integration of the remaining low-dimensional density. The dominant cost of the algorithms is an eigendecomposition computed

SUBMITTER: Greengard P 

PROVIDER: S-EPMC12442500 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature