# Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference

arXiv: https://arxiv.org/abs/2406.17831 · HTML: https://mlsanity.com/paper/2406.17831/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 7 of 7)

- [Distributionally Robust Optimization and Generalization in Kernel Methods](https://mlsanity.com/paper/1905.10943/) (2019)
- [Integer Programming for Learning Directed Acyclic Graphs from Continuous Data](https://mlsanity.com/paper/1904.10574/) (2019)
- [Being Bayesian about Network Structure](https://mlsanity.com/paper/1301.3856/) (2013)
- [Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexity](https://mlsanity.com/paper/1605.04071/) (2016)
- [Consistent Second-Order Conic Integer Programming for Learning Bayesian Networks](https://mlsanity.com/paper/2005.14346/) (2020)
- [Finding All Bayesian Network Structures within a Factor of Optimal](https://mlsanity.com/paper/1811.05039/) (2018)
- [A Risk Management Perspective on Statistical Estimation and Generalized Variational Inference](https://mlsanity.com/paper/2310.17376/) (2023)

## Abstract (arXiv)

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.