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- Bayesian influence by message passing which is written purely in Julia and named ReactiveMP.
- And there is like a brief introduction to the ideas behind ForneyLab and ReactiveMP as well
- GraphPPL.jl package for it has been designed specifically for ReactiveMP
- and it builds foreign style factor graph under the hood which is compatible with ReactiveMP.
- We may use ReactiveMP.jl to run a real-time Bayesian inference on top of that and to
- real ReactiveMP is able to run real-time Bayesian inference for this kind of a model
- It is also possible to use ReactiveMP.jl with static data set. In this example,
- But ReactiveMP.jl scales nicely. We can use, for example ten thousands of observations and it takes
- in our example and they see that ReactiveMP just outperforms both ForneyLab and Turing.
- And our future plan is to combine ReactiveMP with GraphPPL model specification library as a
- It is worth to know that neither ForneyLab nor ReactiveMP are general purpose PPLs
- You can find some demos in ReactiveMP.jl repository under demo folder, for example we
- a small overview of the new library. So we presented ReactiveMP as an inference engine.
