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Learning Bayesian Statistics
Reactive Message Passing in Bayesian Inference
Feb 28, 2024
Exploring reactive message passing in Bayesian inference for real-time data scenarios with unknown structures, including applications like denoising speech and real-time position tracking systems. Discussing the unique characteristics of RX Infar, a Bayesian inference tool inspired by the free energy principle, and the efficiency and speed advantages of using Julia programming language.
08:49
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Quick takeaways
- Reactive message passing in Bayesian inference is beneficial for real-time data processing with uncertain structures, applied in scenarios like denoising speech and controlling drones based on environmental models.
- Choosing Julia for RX inference development offers efficiency and rapid development, leveraging macros for code transformations and simplifying complex probabilistic model specifications.
Deep dives
Reactive message passing with RX inference tool
The podcast discusses the concept of reactive message passing using RX inferences, which proves valuable for processing real-time data with uncertain structures. An example provided is creating probabilistic models for denoising speech or controlling a drone's movements in response to obstacles. The discussion highlights the application of active inference to infer optimal actions based on environmental models, emphasizing scenarios with high uncertainty and the tool's inspiration drawn from a scientific principle defining natural biotic systems.
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Discover
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Listen to the best highlights from the podcasts you love and dive into the full episode