A Variational Approximation for Analyzing the Dynamics of Panel Data.

ABSTRACT: Panel data involving longitudinal measurements of the same set of participants taken over multiple time points is common in studies to understand childhood development and disease modeling. Deep hybrid models that marry the predictive power of neural networks with physical simulators such as differential equations, are starting to drive advances in such applications. The task of modeling not just the observations but the hidden dynamics that are captured by the measurements poses interesting statistical/computational questions. We propose a probabilistic model called ME-NODE to incorporate (fixed + random) mixed effects for analyzing such panel data. We show that our model can be derived using smooth approximations of SDEs provided by the Wong-Zakai theorem. We then derive Evidence Based L

SUBMITTER: Nazarovs J 

PROVIDER: S-EPMC8500136 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature