JuliaUserGroupMunich: Dmitry Bagaev - A path to fast and scalable Bayesian inference

:round_pushpin:online, organized via meetup :spiral_calendar: Monday 1. July 2024 :clock6: CEST 18:00
!!! EXCEPTION: starting half an hour earlier as usual !!!

Fast Bayesian Inference with RxInfer.jl

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After our latest talk on Gen.jl I am very happy to announce this presentation about RxInfer.jl. Gen.jl, Turing.jl, and RxInfer.jl are three major probabilistic frameworks in Julia, all with different core design decisions, and all actively developed.

Dmitry Bagaev @bvdmitri is our guest, the main developer and creator of RxInfer.jl

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:clock6: 18:00 - 19:00. Dmitry Bagaev: A path to fast and scalable Bayesian inference

Given a probabilistic model, RxInfer 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. RxInfer.jl has been designed with a focus on efficiency, scalability and maximum performance for running inference with reactive message passing. It excels in models with conjugate likelihood-prior pairings. RxInfer also supports non-conjugate inference and is continually being improved to handle a broader class of models.

:clock7: 19:00 - 19:30. Time for further discussion

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The event is going to be online. The link will be made available 10 min before the actual start. Please join 5 min ahead, the session is going to start on-time,

Looking forward to see you all,

best,
Stephan Sahm
sponsored by Jolin.io
www.jolin.io

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