Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference

TL;DR

This paper introduces an Empirical Bayes method combined with Generalized Variational Inference to learn Dynamic Bayesian Networks, enabling uncertainty quantification over structures and parameters in a data-driven manner.

Contribution

It presents a novel approach that integrates Empirical Bayes with Generalized Variational Inference for dynamic Bayesian network learning, allowing for uncertainty estimation.

Findings

Demonstrates effective uncertainty quantification in structure and parameter estimates.

Shows potential for sampling mixture of DAG structures.

Provides a data-driven prior framework for Bayesian network learning.

Abstract

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.