mlsanityData to 7 Oct 2026

Patterns of Scalable Bayesian Inference

arXiv 1602.05221 ↗First posted 16 Feb 2016stat.MLcited by 24

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Abstract (arXiv)

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2016.

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Citing papers per year: 2021: 6; 2022: 9; 2023: 2; 2024: 3; 2025: 3; 2026: 1.

24 papers.

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  1. 2021Pathfinder: Parallel quasi-Newton variational inferencecited by 26
  2. 2023FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregationcited by 22
  3. 2022Few-Shot Calibration of Set Predictors via Meta-Learned Cross-Validation-Based Conformal Predictioncited by 17
  4. 2022Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modelingcited by 12
  5. 2022Bayesian Continual Learning via Spiking Neural Networkscited by 12
  6. 2022Parallel MCMC Without Embarrassing Failurescited by 11
  7. 2022Client Selection for Federated Bayesian Learningcited by 4
  8. 2021Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMCcited by 3
  9. 2021Interpolating between sampling and variational inference with infinite stochastic mixturescited by 3
  10. 2022Spatiotemporal Clustering with Neyman-Scott Processes via Connections to Bayesian Nonparametric Mixture Modelscited by 3
  11. 2022Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challengescited by 3
  12. 2023Calibration-Aware Bayesian Learningcited by 3
12 more
  1. 2021Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalizationcited by 2
  2. 2025Uncertainty Quantification and Data Efficiency in AI: An Information-Theoretic Perspectivecited by 2
  3. 2021Bayesian Active Meta-Learning for Black-Box Optimizationcited by 1
  4. 2024Comparison of parallel SMC and MCMC for Bayesian deep learningcited by 1
  5. 2024Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimizationcited by 1
  6. 2025To Trust or Not to Trust: On Calibration in ML-based Resource Allocation for Wireless Networkscited by 1
  7. 2021Robust Distributed Bayesian Learning with Stragglers via Consensus Monte Carlocited by 0
  8. 2022Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamicscited by 0
  9. 2022Calibrating AI Models for Wireless Communications via Conformal Predictioncited by 0
  10. 2024Accelerated Markov Chain Monte Carlo Using Adaptive Weighting Schemecited by 0
  11. 2025Scalable Bayesian Monte Carlo: fast uncertainty estimation beyond deep ensemblescited by 0
  12. 2026A Hardware-oriented Approach for Efficient Bayesian Inference Computation and Deploymentcited by 0