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On Bayesian mechanics: a physics of and by beliefs

Ramstead, Maxwell JD; Sakthivadivel, Dalton AR; Heins, Conor; Koudahl, Magnus; Millidge, Beren; Da Costa, Lancelot; Klein, Brennan; (2023) On Bayesian mechanics: a physics of and by beliefs. Interface Focus , 13 (3) , Article 20220029. 10.1098/rsfs.2022.0029. Green open access

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Abstract

The aim of this paper is to introduce a field of study that has emerged over the last decade, called Bayesian mechanics. Bayesian mechanics is a probabilistic mechanics, comprising tools that enable us to model systems endowed with a particular partition (i.e. into particles), where the internal states (or the trajectories of internal states) of a particular system encode the parameters of beliefs about external states (or their trajectories). These tools allow us to write down mechanical theories for systems that look as if they are estimating posterior probability distributions over the causes of their sensory states. This provides a formal language for modelling the constraints, forces, potentials and other quantities determining the dynamics of such systems, especially as they entail dynamics on a space of beliefs (i.e. on a statistical manifold). Here, we will review the state of the art in the literature on the free energy principle, distinguishing between three ways in which Bayesian mechanics has been applied to particular systems (i.e. path-tracking, mode-tracking and mode-matching). We go on to examine a duality between the free energy principle and the constrained maximum entropy principle, both of which lie at the heart of Bayesian mechanics, and discuss its implications.

Type: Article
Title: On Bayesian mechanics: a physics of and by beliefs
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1098/rsfs.2022.0029
Publisher version: https://doi.org/10.1098/rsfs.2022.0029
Language: English
Additional information: This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
Keywords: Science & Technology, Life Sciences & Biomedicine, Biology, Life Sciences & Biomedicine - Other Topics, free energy principle, active inference, Bayesian mechanics, information geometry, maximum entropy, gauge theory, FREE-ENERGY PRINCIPLE, MAXIMUM-ENTROPY, INFERENCE
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/10170969
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