Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
Machine Learning
2024-07-02 v2 Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.2406.17831,
title = {Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference},
author = {Vyacheslav Kungurtsev and Apaar and Aarya Khandelwal and Parth Sandeep Rastogi and Bapi Chatterjee and Jakub Mareček},
journal= {arXiv preprint arXiv:2406.17831},
year = {2024}
}