# Big Learning with Bayesian Methods

arXiv: https://arxiv.org/abs/1411.6370 · HTML: https://mlsanity.com/paper/1411.6370/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2014.

## Built on by (most cited first, 4 of 4)

- [A Survey of Monte Carlo Methods for Parameter Estimation](https://mlsanity.com/paper/2107.11820/) (2021)
- [Uncertainty Calibration for Counterfactual Propensity Estimation in Recommendation](https://mlsanity.com/paper/2303.12973/) (2023)
- [Federated Variational Inference for Bayesian Mixture Models](https://mlsanity.com/paper/2502.12684/) (2025)
- [Estimation & Recognition under Perspective of Random-Fuzzy Dual Interpretation of Unknown Quantity: with Demonstration of IMM Filter](https://mlsanity.com/paper/2110.10572/) (2021)

## Abstract (arXiv)

Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.