Julia package for automatic Bayesian inference on a factor graph with reactive message passing.

Given a probabilistic model, ReactiveMP allows for an efficient message-passing based Bayesian inference. It uses the model structure to generate an algorithm that consists of a sequence of local computations on a Forney-style factor graph (FFG) representation of the model.

ReactiveMP.jl has been designed with a focus on efficiency, scalability and maximum performance for running inference with message passing. It is worth noting that this package is aimed to run Bayesian inference for conjugate state-space models. For these types of models, ReactiveMP.jl takes advantage of the conjugate pairings and beats general-purpose probabilistic programming packages easily in terms of computational load, speed, memory and accuracy. On the other hand, sampling-based packages like Turing.jl are generic Bayesian inference solutions and are capable of running inference for a broader set of models.

Package Features

  • User friendly syntax for specification of probabilistic models.
  • Automatic generation of message passing algorithms including
  • Support for hybrid models combining discrete and continuous latent variables.
  • Support for hybrid distinct message passing inference algorithm under a unified paradigm.
  • Evaluation of Bethe free energy as a model performance measure.
  • Schedule-free reactive message passing API.
  • High performance.
  • Scalability for large models with millions of parameters and observations.
  • Inference procedure is differentiable.
  • Easy to extend with custom nodes.


How to get started?

Head to the Getting started section to get up and running with ForneyLab. Alternatively, explore various examples in the documentation.

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