Summary of Empirical Bayes For Dynamic Bayesian Networks Using Generalized Variational Inference, by Vyacheslav Kungurtsev et al.
Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
by Vyacheslav Kungurtsev, Apaar, Aarya Khandelwal, Parth Sandeep Rastogi, Bapi Chatterjee, Jakub Mareček
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an Empirical Bayes approach for learning Dynamic Bayesian Networks (DBNs). The authors use point estimates to initialize structure and weights, then employ a data-driven prior to obtain a model that quantifies uncertainty. This method builds upon Generalized Variational Inference and demonstrates the potential of sampling uncertainty in mixture DAG structures and parameter posteriors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to learn Dynamic Bayesian Networks (DBNs) using an Empirical Bayes approach. Instead of starting from scratch, this method uses point estimates to get a good starting point for structure and weights. Then, it uses special math called Generalized Variational Inference to figure out the uncertainty of the model. This could be useful for making predictions and understanding how certain things are. |
Keywords
» Artificial intelligence » Inference
