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Learning Bayesian Statistics

#100 Reactive Message Passing & Automated Inference in Julia, with Dmitry Bagaev

Feb 21, 2024
Dmitry Bagaev discusses reactive message passing in Bayesian inference and the development of RxInfer.jl. He talks about the challenges and benefits, variational inference, and the trade-offs in architecture. Dmitry shares insights into his startup Lazy Dynamics and the future of automated Bayesian inference. Also, his background in Russia, extreme sports hobbies, and the user-friendliness of inference methods are discussed.
54:42

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Quick takeaways

  • Reactive message passing enables real-time signal processing with automatic posterior updates in Bayesian inference.
  • RxInfer.jl addresses scalability challenges in Bayesian models through factor graphs, message passing, and variational inference to enhance efficiency and adaptivity.

Deep dives

Reactive Message Passing in Probabilistic Models

Reactive message passing in probabilistic models, as discussed in the podcast, involves representing the probabilistic model as a graph and sending messages between nodes along edges to solve the base rule. Reactive message passing is unique because it reacts to changes in data and automatically updates the posterior based on new observations, without enforcing a specific order of computations, making it suitable for real-time signal processing. By enabling data-driven message computations, reactive message passing in probabilistic models like rxinfer.julia can efficiently handle changing data streams and update the posterior distribution.

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