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Bayesian mechanics for stationary processes.

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  • 1Department of Mathematics, Imperial College London, London SW7 2AZ, UK.

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Summary
This summary is machine-generated.

This study introduces Bayesian mechanics for adaptive systems using Markov blankets to model system-environment interactions. This framework reveals how internal states infer external states, unifying concepts in Bayesian statistics, neuroscience, and control theory.

Keywords:
Markov blanketactive inferencefree-energy principlenon-equilibrium steady statepredictive processingvariational Bayesian inference

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Area of Science:

  • Theoretical neuroscience
  • Bayesian statistics
  • Adaptive systems engineering
  • Theoretical biology

Background:

  • Adaptive systems require mechanisms to infer and respond to environmental changes.
  • Existing models often treat inference and control separately.
  • A unified framework for understanding adaptive behavior is needed.

Purpose of the Study:

  • To develop a Bayesian mechanics framework for adaptive systems.
  • To model the system-environment interface using Markov blankets.
  • To unify concepts from Bayesian inference, neuroscience, and control theory.

Main Methods:

  • Modeling the system-environment interface with a Markov blanket.
  • Introducing dynamics to represent adaptive systems at steady state.
  • Partitioning the Markov blanket into sensory and active states.

Main Results:

  • Internal states within the Markov blanket encode information about external states.
  • Adaptive systems demonstrate inference of external states, aligning with variational inference.
  • Active states perform active inference and stochastic control (e.g., PID control).

Conclusions:

  • The proposed Bayesian mechanics provides a unified framework for adaptive systems.
  • The model bridges theoretical neuroscience, Bayesian statistics, and engineering control.
  • This work offers novel insights into the fundamental principles of adaptive behavior.