[ABE-L] Convite: Seminário DEST/UFMG em 06/05/2022.

[Vinicius Mayrink]

Caros,

Na próxima sexta-feira (*06 de maio*, às *13:30h*) o ciclo de
Seminários do *Departamento
de Estatística da UFMG* terá a apresentação de *Mark D. Risser*.

Mark é pesquisador na Climate and Ecosystem Sciences Division do Lawrence
Berkeley National Laboratory (Berkeley, CA, EUA). Ele obteve o grau de
Doutor em Estatística pela Ohio State University (EUA). Suas principais
áreas de pesquisa são: Modelagem Bayesiana, Estatística Espacial, Ambiental
e Educacional, Métodos computacionais, Equações diferenciais estocásticas,
visualização de dados e Meta análise.

O seminário será transmitido ao vivo pelo canal do Youtube "Seminários DEST
- UFMG <https://www.youtube.com/channel/UCoZC2_pME9ca_-Hx4djd60w>".

At.te,
Vinícius Mayrink


*********** Título e Resumo ***********
Mark D. Risser (Lawrence Berkeley National Laboratory, EUA)


*Bayesian inference for high-dimensional nonstationary Gaussian processes.*
In spite of the diverse literature on nonstationary spatial modelling and
approximate Gaussian process (GP) methods, there are no general approaches
for conducting fully Bayesian inference for moderately sized nonstationary
spatial data sets on a personal laptop. For statisticians and data
scientists who wish to conduct posterior inference and prediction with
appropriate uncertainty quantification, the lack of such approaches and
software is a limitation. In this work, we develop methodology for
implementing formal Bayesian inference for a general class of nonstationary
GPs. Our novel approach uses pre-existing frameworks for characterizing
nonstationarity in a new way while utilizing via modern GP likelihood
approximations. Posterior sampling is implemented using flexible MCMC
methods, with nonstationary posterior prediction conducted as a
post-processing step. We demonstrate our novel methods on three data sets,
ranging from several hundred to over several thousand locations. All of our
methods are implemented in the freely available BayesNSGP software package
for R.

--
*Vinícius D. Mayrink*
*Professor Associado - Departamento de Estatística*

*ICEx, Universidade Federal de Minas Gerais*
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