Beyond prediction: A framework for inference with variational approximations in mixture models.

ABSTRACT: Variational inference is a popular method for estimating model parameters and conditional distributions in hierarchical and mixed models, which arise frequently in many settings in the health, social, and biological sciences. Variational inference in a frequentist context works by approximating intractable conditional distributions with a tractable family and optimizing the resulting lower bound on the log-likelihood. The variational objective function is typically less computationally intensive to optimize than the true likelihood, enabling scientists to fit rich models even with extremely large datasets. Despite widespread use, little is known about the general theoretical properties of estimators arising from variational approximations to the log-likelihood, which hinders their use in i

SUBMITTER: Westling T 

PROVIDER: S-EPMC7380652 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature