Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
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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.
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References · 7
- 2019Distributionally Robust Optimization and Generalization in Kernel Methodscited by 73
- 2019Integer Programming for Learning Directed Acyclic Graphs from Continuous Datacited by 20
- 2013Being Bayesian about Network Structurecited by 17
- 2016Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets, and Complexitycited by 9
- 2020Consistent Second-Order Conic Integer Programming for Learning Bayesian Networkscited by 9
- 2018Finding All Bayesian Network Structures within a Factor of Optimalcited by 3
- 2023A Risk Management Perspective on Statistical Estimation and Generalized Variational Inferencecited by 2
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