ReacitveMP.jl
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.
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.
Resources
- For an introduction to message passing and FFGs, see The Factor Graph Approach to Model-Based Signal Processing by Loeliger et al. (2007).
How to get started?
Head to the Getting started section to get up and running with ForneyLab.
Table of Contents
- Getting started
- Model Specification
- Messages implementation
- Nodes implementation
- Math utilities
- Contribution guidelines
Index
ReactiveMP.hugeReactiveMP.tinyReactiveMP.AbstractMessageReactiveMP.DeterministicReactiveMP.HugeNumberReactiveMP.MessageReactiveMP.OneDivNVectorReactiveMP.StochasticReactiveMP.TinyNumberReactiveMP.UndefinedNodeFunctionalFormReactiveMP.ValidNodeFunctionalFormReactiveMP.as_node_functional_formReactiveMP.isdeterministicReactiveMP.isstochasticReactiveMP.sdtype