Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866911937557692416 |
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| author | Kungurtsev, Vyacheslav Apaar Khandelwal, Aarya Rastogi, Parth Sandeep Chatterjee, Bapi Mareček, Jakub |
| author_facet | Kungurtsev, Vyacheslav Apaar Khandelwal, Aarya Rastogi, Parth Sandeep Chatterjee, Bapi Mareček, Jakub |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17831 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference Kungurtsev, Vyacheslav Apaar Khandelwal, Aarya Rastogi, Parth Sandeep Chatterjee, Bapi Mareček, Jakub Machine Learning Statistics Theory 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. |
| title | Empirical Bayes for Dynamic Bayesian Networks Using Generalized Variational Inference |
| topic | Machine Learning Statistics Theory |
| url | https://arxiv.org/abs/2406.17831 |