Paper ID: 2406.17831

Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference

Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal, Parth Sandeep Rastogi, Bapi Chatterjee, Jakub Mareček

In this work, we demonstrate the Empirical Bayes approach to learning a Dynamic Bayesian Network. By starting with several point estimates of structure and weights, we can use a data-driven prior to subsequently obtain a model to quantify uncertainty. This approach uses a recent development of Generalized Variational Inference, and indicates the potential of sampling the uncertainty of a mixture of DAG structures as well as a parameter posterior.

Submitted: Jun 25, 2024