Probabilistic morphisms and Bayesian supervised learning
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985840%3A_____%2F25%3A00637597" target="_blank" >RIV/67985840:_____/25:00637597 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.4213/sm10191e" target="_blank" >https://doi.org/10.4213/sm10191e</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.4213/sm10191e" target="_blank" >10.4213/sm10191e</a>
Alternative languages
Result language
angličtina
Original language name
Probabilistic morphisms and Bayesian supervised learning
Original language description
We develop the category theory of Markov kernels to the study of categorical aspects of Bayesian inversions. As a result, we present a unified model for Bayesian supervised learning, including Bayesian density estimation. We illustrate this model with Gaussian process regressions.
Czech name
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Czech description
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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
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Sbornik Mathematics
ISSN
1064-5616
e-ISSN
1468-4802
Volume of the periodical
216
Issue of the periodical within the volume
5
Country of publishing house
GB - UNITED KINGDOM
Number of pages
19
Pages from-to
723-741
UT code for WoS article
001578022100008
EID of the result in the Scopus database
2-s2.0-105012498077