Papers › DiBS: Differentiable Bayesian Structure Learning

DiBS: Differentiable Bayesian Structure Learning

25 May 2021NeurIPS 2021 12arXiv:2105.11839archive 2025-07-28

Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause

Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and the conditional distribution parameters. This makes our formulation directly applicable to posterior inference of complex Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks. Using DiBS, we devise an efficient, general purpose variational inference method for approximating distributions over structural models. In evaluations on simulated and real-world data, our method significantly outperforms related approaches to joint posterior inference.

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2ran · our draft was wrong
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acyclic_constr_nograd larslorch/dibs/dibs/inference/dibs.py official repository ran · fixture could not drive it fingerprinted MIT (permissive) · 417b589baddd85f9 · report
expand_by larslorch/dibs/dibs/inference/dibs.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 3d92bdb5e57a28c1 · report
zero_diagonal larslorch/dibs/dibs/inference/dibs.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · 2febe9020c5e0a67 · report
DiBS larslorch/dibs/dibs/inference/dibs.py official repository unverified MIT (permissive) · e943fa446eeb8780 · report

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Causal DiscoveryVariational Inference

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Variational Inference

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