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Abstract
ReactiveMP.jl is a native Julia implementation of reactive message passing-based Bayesian inference in probabilistic graphical models with Factor Graphs. The package does Constrained Bethe Free Energy minimisation and supports both exact and variational Bayesian inference, provides a convenient syntax for model specification and allows for extra factorisation and form constraints specification of the variational family of distributions. In addition, ReactiveMP.jl includes a large range of standard probabilistic models and can easily be extended to custom novel nodes and message update rules. In contrast to non-reactive (imperatively coded) Bayesian inference packages, ReactiveMP.jl scales easily to support inference on a standard laptop for large conjugate models with tens of thousands of variables and millions of nodes.
Original language | English |
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Number of pages | 2 |
Journal | JuliaCon Proceedings |
Volume | 1 |
Issue number | 1 |
DOIs | |
Publication status | Published - 29 Jan 2022 |
Keywords
- Bayesian Inference,
- Julia
- Factor Graphs
- Graphical Models
- Free Energy Minimzation
- Message Passing
- Reactive Programming
- variational inference
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Dive into the research topics of 'ReactiveMP.jl: A Julia Package for Reactive Message Passing-based Bayesian Inference'. Together they form a unique fingerprint.Activities
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ReactiveMP.jl: Reactive Message Passing-based Bayesian Inference | Dmitry Bagaev | JuliaCon2021
Bagaev, D. (Speaker)
28 Jul 2021Activity: Talk or presentation types › Contributed talk › Professional