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pro vyhledávání: '"Lee Jaeyong"'
In this paper, we provide a theoretical analysis of a type of operator learning method without data reliance based on the classical finite element approximation, which is called the finite element operator network (FEONet). We first establish the con
Externí odkaz:
http://arxiv.org/abs/2404.17868
In this paper, we propose a scalable Bayesian method for sparse covariance matrix estimation by incorporating a continuous shrinkage prior with a screening procedure. In the first step of the procedure, the off-diagonal elements with small correlatio
Externí odkaz:
http://arxiv.org/abs/2206.12773
Autor:
Lee, Kyeongwon, Lee, Jaeyong
Neural networks have shown great predictive power when dealing with various unstructured data such as images and natural languages. The Bayesian neural network captures the uncertainty of prediction by putting a prior distribution for the parameter o
Externí odkaz:
http://arxiv.org/abs/2206.00241
In this paper, we estimate the seroprevalence against COVID-19 by country and derive the seroprevalence over the world. To estimate seroprevalence, we use serological surveys (also called the serosurveys) conducted within each country. When the seros
Externí odkaz:
http://arxiv.org/abs/2201.13124
Autor:
Sung, Bongjung, Lee, Jaeyong
Gaussian covariance graph model is a popular model in revealing underlying dependency structures among random variables. A Bayesian approach to the estimation of covariance structures uses priors that force zeros on some off-diagonal entries of covar
Externí odkaz:
http://arxiv.org/abs/2111.02637
Autor:
Lee, Jaeyong
In this article, I introduce the differential equation model and review their frequentist and Bayesian computation methods. A numerical example of the FitzHugh-Nagumo model is given.
Externí odkaz:
http://arxiv.org/abs/2110.04726
Autor:
Bon, Christopher G., Grigg, Jason C., Lee, Jaeyong, Robb, Craig S., Caveney, Nathanael A., Eltis, Lindsay D., Strynadka, Natalie C.J.
Publikováno v:
In Journal of Structural Biology June 2024 216(2)
Autor:
Park, Sewon, Lee, Jaeyong
We develop a fully Bayesian nonparametric regression model based on a L\'evy process prior named MLABS (Multivariate L\'evy Adaptive B-Spline regression) model, a multivariate version of the LARK (L\'evy Adaptive Regression Kernels) models, for estim
Externí odkaz:
http://arxiv.org/abs/2108.11863
Autor:
Lee, Kwangmin, Lee, Jaeyong
We consider Bayesian inference of sparse covariance matrices and propose a post-processed posterior. This method consists of two steps. In the first step, posterior samples are obtained from the conjugate inverse-Wishart posterior without considering
Externí odkaz:
http://arxiv.org/abs/2108.09462
Autor:
Kim, Jaehoan, Lee, Jaeyong
Publikováno v:
Journal of the Korean Data & Information Science Society, vol.32, no.6 (2021), 1373-1392
Gaussian process regression (GPR) model is a popular nonparametric regression model. In GPR, features of the regression function such as varying degrees of smoothness and periodicities are modeled through combining various covarinace kernels, which a
Externí odkaz:
http://arxiv.org/abs/2108.04715