3 citations · 4 across the 8 of their papers we have counts for
9 papers
Coherent and robust Bayesian inference under informative sampling via sample-level weight models
Kosuke Morikawa, Jae Kwang Kim, Won Chang
We develop two complementary Bayesian procedures for analytic inference under informative sampling. The first constructs a coherent likelihood by modeling the sample-level distribu…
Synthetic Data Generation With Incomplete Survey Data Under Informative Sampling
Ayat Almomani, Won Chang, Youngdeok Hwang +2
We propose a Bayesian framework for data synthesis and imputation in complex survey settings with informative sampling. To address variance underestimation in existing Bayesian app…
Stick-Breaking Mixture Normalizing Flows with Component-Wise Tail Adaptation for Variational Inference
Seungsu Han, Juyoung Hwang, Won Chang
Normalizing flows with a Gaussian base provide a computationally efficient way to approximate posterior distributions in Bayesian inference, but they often struggle to capture comp…
Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives
Hyeongseon Jeon, Juan Xie, Yeseul Jeon +4
Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target…
Fast Computer Model Calibration using Annealed and Transformed Variational Inference
Dongkyu Derek Cho, Won Chang, Jaewoo Park
Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameter…
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.…
A Spatio-Temporal Dirichlet Process Mixture Model for Coronavirus Disease-19
Jaewoo Park, Seorim Yi, Won Chang +1
Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide r…
graph-GPA 2.0: A Graphical Model for Multi-disease Analysis of GWAS Results with Integration of Functional Annotation Data
Qiaolan Deng, Jin Hyun Nam, Ayse Selen Yilmaz +5
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to…
An Interaction Neyman-Scott Point Process Model for Coronavirus Disease-19
J. Park, W. Chang, B. Choi
With rapid transmission, the coronavirus disease 2019 (COVID-19) has led to over 2 million deaths worldwide, posing significant societal challenges. Understanding the spatial patte…