Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
<|reference_start|>Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference: 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.<|reference_end|>
@article{kungurtsev2024empirical,
    title={Empirical Bayes for Dynamic Bayesian Networks Using Generalized
  Variational Inference},
    author={Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal, Parth Sandeep Rastogi,
  Bapi Chatterjee, Jakub Marev{c}ek},
    journal={arXiv preprint arXiv:2406.17831},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17831},
    primaryClass={cs.LG math.ST stat.TH}
}