Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Onur Teymur"'
Publikováno v:
Annual Review of Statistics and Its Application. 9:529-555
Markov chain Monte Carlo (MCMC) is the engine of modern Bayesian statistics, being used to approximate the posterior and derived quantities of interest. Despite this, the issue of how the output from a Markov chain is post-processed and reported is o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9e5a6a6f662492ec96a103cc4c2bf3c
https://doi.org/10.1146/annurev-statistics-040220-091727
https://doi.org/10.1146/annurev-statistics-040220-091727
Autor:
Sarah Filippi, Onur Teymur
This article introduces a Bayesian nonparametric method for quantifying the relative evidence in a dataset in favour of the dependence or independence of two variables conditional on a third. The approach uses Polya tree priors on spaces of condition
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16503dee32a355df4158adbb80191a6f
https://kar.kent.ac.uk/90450/1/2639-8001_2020_2_155.pdf
https://kar.kent.ac.uk/90450/1/2639-8001_2020_2_155.pdf
Autor:
François-Xavier Briol, Jon Cockayne, Onur Teymur, William Weimin Yoo, Michael Schober, Philipp Hennig
Publikováno v:
Bayesian Analysis
Bayesian Anal. 11, no. 4 (2016), 1285-1293
Bayesian Anal. 11, no. 4 (2016), 1285-1293
We commend the authors for an exciting paper which provides a strong contribution to the emerging field of probabilistic numerics. Below, we discuss aspects of prior modelling for differential equations which will need to be considered thoroughly in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e6eaaf0cad6d5f590cf6f8a0aef26767
http://hdl.handle.net/10044/1/53207
http://hdl.handle.net/10044/1/53207
Publikováno v:
Scopus-Elsevier
University of Helsinki
Advances in Neural Information Processing Systems
University of Helsinki
Advances in Neural Information Processing Systems
Probabilistic numerics casts numerical tasks, such the numerical solution of differential equations, as inference problems to be solved. One approach is to model the unknown quantity of interest as a random variable, and to constrain this variable us
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e145dbb0f10e095e8e33c5ed81da20f4
http://www.scopus.com/inward/record.url?eid=2-s2.0-85132031646&partnerID=MN8TOARS
http://www.scopus.com/inward/record.url?eid=2-s2.0-85132031646&partnerID=MN8TOARS