4 citations · 5 across the 11 of their papers we have counts for
8 papers · 1 filter
Adaptive Functional Clustering with Structured Dependence via Variational Inference
Seojin Lee, Neulpum Jeong, Seonghyun Jeong
Functional clustering is an important tool for identifying latent heterogeneity in functional data and has been widely applied across various scientific fields. However, many exist…
Bayesian Triangulation Splines: Spatial Adaptation on Irregular Domains
Sihyeon Pyeon, Sunwoo Lim, Seonghyun Jeong
Conventional nonparametric regression methods for two-dimensional non-rectangular domains often overlook domain geometry and allow smoothing across boundaries. In spatial and geost…
Asymptotically Valid Laplace Variational Inference for Bayesian Nonparametric Clustering of Marked Point Patterns
Minsung Choi, Seonghyun Jeong
We introduce Dirichlet process mixtures of marked Poisson point processes, a Bayesian nonparametric model for clustering replicated marked point patterns. The model jointly infers…
Bayesian Nonparametric Modeling for Multivariate Conditional Copula Regression with Varying Coefficients
Yujin Jeong, Seonghyun Jeong
Multivariate mixed-type outcomes are difficult to model jointly, and additional complexity arises when both marginal effects and dependence structures vary with a covariate such as…
Model selection-based estimation for generalized additive models using mixtures of g-priors: Towards systematization
Gyeonghun Kang, Seonghyun Jeong
We explore the estimation of generalized additive models using basis expansion in conjunction with Bayesian model selection. Although Bayesian model selection is useful for regress…
Functional clustering methods for binary longitudinal data with temporal heterogeneity
Jinwon Sohn, Seonghyun Jeong, Young Min Cho +1
In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating sim…
A Bayesian Convolutional Neural Network-based Generalized Linear Model
Yeseul Jeon, Won Chang, Seonghyun Jeong +2
Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data.…
Bayesian model selection in additive partial linear models via locally adaptive splines
Seonghyun Jeong, Taeyoung Park, David A. van Dyk
We provide a flexible framework for selecting among a class of additive partial linear models that allows both linear and nonlinear additive components. In practice, it is challeng…