Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network

[2305.19366] Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network Skip to main content Search Submit Donate Log in Computer Science > Machine Learning arXiv:2305.19366 (cs) [Submitted on 30 May 2023 ( v1 ), last revised 28 Aug 2026 (this version, v3)] Title: Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network Authors: Tristan Deleu , Mizu Nishikawa-Toomey , Jithendaraa Subramanian , Esmeralda S. Whitammer , Laurent Charlin , Yoshua Bengio View PDF HTML (experimental) Abstract: Generative Flow Networks (GFlowNets), a class of generative models over discrete and structured sample spaces, have been previously applied to the problem of inferring the marginal posterior distribution over the directed acyclic graph (DAG) of a Bayesian Network, given a dataset of observations. Based on recent advances extending this framework to non-discrete sample spaces, we propose in this paper to approximate the joint posterior over not only the structure of a Bayesian Network, but also the parameters of its conditional probability distributions. We use a single GFlowNet whose sampling policy follows a two-phase process: the DAG is first generated sequentially one edge at a time, and then the corresponding parameters are picked once the full structure is known. Since the parameters are included in the posterior distribution, this leaves more flexibility for the local probability models of the Bayesian Network, making our approach applicable even to non-linear models parametrized by neural networks. We show that our method, called JSP-GFN, offers an accurate approximation of the joint posterior, while comparing favorably against existing methods on both simulated and real data. Comments: NeurIPS 2023 Subjects: Machine Learning (cs.LG) ; Machine Learning (stat.ML) Cite as: arXiv:2305.19366 [cs.LG] (or arXiv:2305.19366v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2305.19366 Focus to learn more arXiv-issued DOI via DataCite Related DOI : https://doi.org/10.52202/075280-1360 Focus to learn more DOI(s) linking to related resources Submission history From: Esmeralda Whitammer [ view email ] [v1] Tue, 30 May 2023 19:16:44 UTC (922 KB) [v2] Mon, 30 Oct 2023 14:29:44 UTC (939 KB) [v3] Fri, 28 Aug 2026 06:29:59 UTC (934 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG < prev | next > new | recent | 2023-05 Change to browse by: cs stat stat.ML References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from (opens in new tab) (opens in new tab) (opens in new tab)

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