mlsanityData to 14 Oct 2026

Machine Learning and the Future of Bayesian Computation

arXiv 2304.11251 ↗First posted 21 Apr 2023stat.ML cs.LGcited by 4

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Abstract (arXiv)

Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high dimensional models with many observations. In this article we discuss the potential to improve posterior computation using ideas from machine learning. Concrete future directions are explored in vignettes on normalizing flows, Bayesian coresets, distributed Bayesian inference, and variational inference.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 92 of its 173 references that are in the map. First posted on arXiv 21 Apr 2023.

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92 of 173 references.

References · 92

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38 more
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Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024Stable Training of Normalizing Flows for High-dimensional Variational Inferencecited by 6
  2. 2024One-Shot Federated Learning with Bayesian Pseudocoresetscited by 1
  3. 2025Predictive Coresetscited by 0
  4. 2025On Reconstructing Training Data From Bayesian Posteriors and Trained Modelscited by 0