Open source software for reactive, efficient and scalable Bayesian inference.
Welcome to the Reactive Bayes organization! We develop and maintain a suite of Julia packages for probabilistic programming and Bayesian inference with a focus on reactive and message-passing based inference algorithms.
- RxInfer The central high-level probabilistic programming framework that enables reactive Bayesian inference on factor graphs through reactive message passing. It integrates tools including probabilistic graphical model definition and reactive message-based Bayesian inference.
- ReactiveMP An efficient message passing-based Bayesian inference engine that implements the core algorithms and rules for performing high-performance, reactive message passing on factor graphs.
- GraphPPL A probabilistic programming language for specifying probabilistic graphical models as factor graphs. It provides a high-level domain-specific language for model creation and works seamlessly with RxInfer for inference.
- ExponentialFamily Extends Distributions.jl with comprehensive implementations of a collection of exponential family distributions.
- Rocket A reactive programming framework providing core primitives for event handling and streaming for Julia, inspired by RxJS. It forms the backbone for reactive computations in the ecosystem.
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ExponentialFamilyManifolds Provides manifold representations of exponential family distributions, enabling advanced optimization workflows with Manopt.jl. It facilitates efficient parameter tuning in natural parameter spaces.
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ExponentialFamilyProjection Enables projection of (un-normalized) log probability density functions onto exponential family distributions. It uses optimization techniques from Manopt.jl and leverages ExponentialFamilyManifolds.jl.
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BayesBase Defines and re-exports core methods and utilities for Bayesian computation. It provides a shared foundation for the Reactive Bayes ecosystem.
Our packages are designed to work together seamlessly to provide:
- Reactive Inference: Real-time updates as new data arrives
- Message Passing: Efficient inference through local computations
- Scalability: Handle large models and datasets
- Flexibility: Mix and match components for your specific needs
We welcome contributions from the community! Check out our individual package repositories for more details on how to get involved.
