Variational Inference for Longitudinal Data Using Normalizing Flows
This work addresses the problem of handling complex longitudinal data for researchers in fields like healthcare or finance, though it appears incremental as it builds on existing variational inference and normalizing flow techniques.
The paper tackled modeling high-dimensional longitudinal data by introducing a latent variable generative model using variational inference and normalizing flows, achieving better likelihood estimates and more reliable missing data imputation on 6 datasets.
This paper introduces a new latent variable generative model able to handle high dimensional longitudinal data and relying on variational inference. The time dependency between the observations of an input sequence is modelled using normalizing flows over the associated latent variables. The proposed method can be used to generate either fully synthetic longitudinal sequences or trajectories that are conditioned on several data in a sequence and demonstrates good robustness properties to missing data. We test the model on 6 datasets of different complexity and show that it can achieve better likelihood estimates than some competitors as well as more reliable missing data imputation. A code is made available at \url{https://github.com/clementchadebec/variational_inference_for_longitudinal_data}.