Bayesian Mechanics for Stationary Processes

Overview

Journal Proc Math Phys Eng Sci

Date 2022 Feb 14

PMID 35153603

Authors

Lancelot Da Costa

Karl Friston

Conor Heins

Grigorios A Pavliotis

Affiliations

Soon will be listed here.

Abstract

This paper develops a Bayesian mechanics for adaptive systems. Firstly, we model the interface between a system and its environment with a Markov blanket. This affords conditions under which states internal to the blanket encode information about external states. Second, we introduce dynamics and represent adaptive systems as Markov blankets at steady state. This allows us to identify a wide class of systems whose internal states appear to infer external states, consistent with variational inference in Bayesian statistics and theoretical neuroscience. Finally, we partition the blanket into sensory and active states. It follows that active states can be seen as performing active inference and well-known forms of stochastic control (such as PID control), which are prominent formulations of adaptive behaviour in theoretical biology and engineering.

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