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Probabilistic morphisms and Bayesian nonparametrics

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985840%3A_____%2F21%3A00541937" target="_blank" >RIV/67985840:_____/21:00541937 - isvavai.cz</a>

  • Result on the web

    <a href="https://doi.org/10.1140/epjp/s13360-021-01427-7" target="_blank" >https://doi.org/10.1140/epjp/s13360-021-01427-7</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1140/epjp/s13360-021-01427-7" target="_blank" >10.1140/epjp/s13360-021-01427-7</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Probabilistic morphisms and Bayesian nonparametrics

  • Original language description

    In this paper we develop a functorial language of probabilistic morphisms and apply it to some basic problems in Bayesian nonparametrics. First we extend and unify the Kleisli category of probabilistic morphisms proposed by Lawvere and Giry with the category of statistical models proposed by Chentsov and Morse–Sacksteder. Then we introduce the notion of a Bayesian statistical model that formalizes the notion of a parameter space with a given prior distribution in Bayesian statistics. We revisit the existence of a posterior distribution, using probabilistic morphisms. In particular, we give an explicit formula for posterior distributions of the Bayesian statistical model, assuming that the underlying parameter space is a Souslin space and the sample space is a subset in a complete connected finite dimensional Riemannian manifold. Then we give a new proof of the existence of Dirichlet measures over any measurable space using a functorial property of the Dirichlet map constructed by Sethuraman.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10101 - Pure mathematics

Result continuities

  • Project

    <a href="/en/project/GC18-01953J" target="_blank" >GC18-01953J: Geometric methods in statistical learning theory and applications</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2021

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    European Physical Journal Plus

  • ISSN

    2190-5444

  • e-ISSN

    2190-5444

  • Volume of the periodical

    136

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    DE - GERMANY

  • Number of pages

    29

  • Pages from-to

    441

  • UT code for WoS article

    000647197900002

  • EID of the result in the Scopus database

    2-s2.0-85104854782