Bayesian mechanics for stationary processes

Lade...
Vorschaubild
Dateien
DaCosta_2-71cm7stsluyo6.pdf
DaCosta_2-71cm7stsluyo6.pdfGröße: 2.05 MBDownloads: 153
Datum
2021
Autor:innen
Da Costa, Lancelot
Friston, Karl
Pavliotis, Grigorios A.
Herausgeber:innen
Kontakt
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
ArXiv-ID
Internationale Patentnummer
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Open Access Hybrid
Core Facility der Universität Konstanz
Gesperrt bis
Titel in einer weiteren Sprache
Publikationstyp
Zeitschriftenartikel
Publikationsstatus
Published
Erschienen in
Proceedings of the Royal Society of London, Series A : Mathematical, Physical and Engineering Sciences. Royal Society of London. 2021, 477(2256), 20210518. ISSN 1364-5021. eISSN 1471-2946. Available under: doi: 10.1098/rspa.2021.0518
Zusammenfassung

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.

Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
570 Biowissenschaften, Biologie
Schlagwörter
Konferenz
Rezension
undefined / . - undefined, undefined
Forschungsvorhaben
Organisationseinheiten
Zeitschriftenheft
Datensätze
Zitieren
ISO 690DA COSTA, Lancelot, Karl FRISTON, Conor HEINS, Grigorios A. PAVLIOTIS, 2021. Bayesian mechanics for stationary processes. In: Proceedings of the Royal Society of London, Series A : Mathematical, Physical and Engineering Sciences. Royal Society of London. 2021, 477(2256), 20210518. ISSN 1364-5021. eISSN 1471-2946. Available under: doi: 10.1098/rspa.2021.0518
BibTex
@article{DaCosta2021Bayes-55927,
  year={2021},
  doi={10.1098/rspa.2021.0518},
  title={Bayesian mechanics for stationary processes},
  number={2256},
  volume={477},
  issn={1364-5021},
  journal={Proceedings of the Royal Society of London, Series A : Mathematical, Physical and Engineering Sciences},
  author={Da Costa, Lancelot and Friston, Karl and Heins, Conor and Pavliotis, Grigorios A.},
  note={Article Number: 20210518}
}
RDF
<rdf:RDF
    xmlns:dcterms="http://purl.org/dc/terms/"
    xmlns:dc="http://purl.org/dc/elements/1.1/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:bibo="http://purl.org/ontology/bibo/"
    xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#"
    xmlns:foaf="http://xmlns.com/foaf/0.1/"
    xmlns:void="http://rdfs.org/ns/void#"
    xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > 
  <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/55927">
    <dc:rights>terms-of-use</dc:rights>
    <dc:creator>Pavliotis, Grigorios A.</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dc:creator>Heins, Conor</dc:creator>
    <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <dcterms:issued>2021</dcterms:issued>
    <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/55927"/>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/28"/>
    <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55927/1/DaCosta_2-71cm7stsluyo6.pdf"/>
    <dc:contributor>Da Costa, Lancelot</dc:contributor>
    <dcterms:title>Bayesian mechanics for stationary processes</dcterms:title>
    <dc:contributor>Friston, Karl</dc:contributor>
    <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/43615"/>
    <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/>
    <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-20T08:41:06Z</dcterms:available>
    <dc:contributor>Heins, Conor</dc:contributor>
    <dc:language>eng</dc:language>
    <dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
    <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/55927/1/DaCosta_2-71cm7stsluyo6.pdf"/>
    <dc:contributor>Pavliotis, Grigorios A.</dc:contributor>
    <dc:creator>Da Costa, Lancelot</dc:creator>
    <dc:creator>Friston, Karl</dc:creator>
    <dcterms:rights rdf:resource="https://rightsstatements.org/page/InC/1.0/"/>
    <foaf:homepage rdf:resource="http://localhost:8080/"/>
    <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2021-12-20T08:41:06Z</dc:date>
  </rdf:Description>
</rdf:RDF>
Interner Vermerk
xmlui.Submission.submit.DescribeStep.inputForms.label.kops_note_fromSubmitter
Kontakt
URL der Originalveröffentl.
Prüfdatum der URL
Prüfungsdatum der Dissertation
Finanzierungsart
Kommentar zur Publikation
Allianzlizenz
Corresponding Authors der Uni Konstanz vorhanden
Internationale Co-Autor:innen
Universitätsbibliographie
Begutachtet
Ja
Diese Publikation teilen