Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference

Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal, Parth Sandeep Rastogi, Bapi Chatterjee +1 more
6/25/2024
cs.LGmath.ST

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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.

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Cite this paper

@article{kungurtsev2024empirical,
  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},
  year   = {2024},
  eprint = {2406.17831},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2406.17831v2}
}

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