# Reactive Message Passing for Scalable Bayesian Inference

arXiv: https://arxiv.org/abs/2112.13251 · HTML: https://mlsanity.com/paper/2112.13251/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 52 references that are in the map.
- First posted on arXiv 25 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Advances in Variational Inference](https://mlsanity.com/paper/1711.05597/) (2017)
- [Temporal Parallelization of Bayesian Smoothers](https://mlsanity.com/paper/1905.13002/) (2019)
- [PushNet: Efficient and Adaptive Neural Message Passing](https://mlsanity.com/paper/2003.02228/) (2020)
- [Sigma-Point Filtering and Smoothing Based Parameter Estimation in Nonlinear Dynamic Systems](https://mlsanity.com/paper/1504.06173/) (2015)
- [Reactive Probabilistic Programming](https://mlsanity.com/paper/1908.07563/) (2019)

## Built on by (most cited first, 16 of 16)

- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2504.14898/) (2025)
- [Realising Synthetic Active Inference Agents, Part II: Variational Message Updates](https://mlsanity.com/paper/2306.02733/) (2023)
- [A Message Passing Realization of Expected Free Energy Minimization](https://mlsanity.com/paper/2508.02197/) (2025)
- [Realising Synthetic Active Inference Agents, Part I: Epistemic Objectives and Graphical Specification Language](https://mlsanity.com/paper/2306.08014/) (2023)
- [Toward Design of Synthetic Active Inference Agents by Mere Mortals](https://mlsanity.com/paper/2307.14145/) (2023)
- [Active inference and artificial reasoning](https://mlsanity.com/paper/2512.21129/) (2025)
- [Online Bayesian system identification in multivariate autoregressive models via message passing](https://mlsanity.com/paper/2506.02710/) (2025)
- [Composing Non-Conjugate Factor Graphs with Closed-Form Variational Inference](https://mlsanity.com/paper/2605.29467/) (2026)
- [AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms](https://mlsanity.com/paper/2112.13366/) (2021)
- [Automating Model Comparison in Factor Graphs](https://mlsanity.com/paper/2306.05965/) (2023)
- [Active Inference is a Subtype of Variational Inference](https://mlsanity.com/paper/2511.18955/) (2025)
- [Spike-Timing-Dependent Plasticity for Bernoulli Message Passing](https://mlsanity.com/paper/2512.23728/) (2025)
- [Expected Free Energy-based Planning as Variational Inference](https://mlsanity.com/paper/2606.20658/) (2026)
- [A Factor Graph Approach to Scalable Multi-Output Gaussian Process Regression](https://mlsanity.com/paper/2608.11917/) (2026)
- [Information Geometry of Message Passing](https://mlsanity.com/paper/2608.15922/) (2026)
- [Spike-based Belief Propagation in Nonlinear Dynamical Systems](https://mlsanity.com/paper/2608.19907/) (2026)

## Abstract (arXiv)

We introduce Reactive Message Passing (RMP) as a framework for executing schedule-free, robust and scalable message passing-based inference in a factor graph representation of a probabilistic model. RMP is based on the reactive programming style that only describes how nodes in a factor graph react to changes in connected nodes. The absence of a fixed message passing schedule improves robustness, scalability and execution time of the inference procedure. We also present ReactiveMP.jl, which is a Julia package for realizing RMP through minimization of a constrained Bethe free energy. By user-defined specification of local form and factorization constraints on the variational posterior distribution, ReactiveMP.jl executes hybrid message passing algorithms including belief propagation, variational message passing, expectation propagation, and expectation maximisation update rules. Experimental results demonstrate the improved performance of ReactiveMP-based RMP in comparison to other Julia packages for Bayesian inference across a range of probabilistic models. In particular, we show that the RMP framework is able to run Bayesian inference for large-scale probabilistic state space models with hundreds of thousands of random variables on a standard laptop computer.