Welcome to Bayesian Statistical Learning Lab

Welcome to the Bayesian Statistical Learning Lab at Kyungpook National University, South Korea.

Our lab advances Bayesian statistics and statistical learning through innovative research in statistical theory, computational methods, and real-world applications. We focus on developing reliable and scalable methodologies for complex and high-dimensional data, with particular interests in Bayesian computation, high-dimensional statistical learning, and causal inference.

We are committed to fostering an intellectually stimulating and collaborative research environment where theoretical innovation meets practical challenges.

Principal Investigator

Gyuhyeong Goh, Ph.D.

Associate Professor

Department of Statistics

Kyungpook National University

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Selected publications

S. Jin & G. Goh (2026). “Hybrid Best Subset Selection for Bayesian Multivariate Regression ”, Journal of Chemometrics, 40 (11), e70184

G. Goh, J. Yu, M. Kim & J. Tack (2026). “Bayesian space–time varying coefficient modeling for climate econometrics: A spatial–temporal Gaussian process approach”, Journal of Econometrics, 256, 106075.

G. Goh (2026) "Admissibility of generalized Bayes estimators in linear regression with hierarchical g-priors", Statistica Neerlandica, 80 (2), e70027

W. Duan & G. Goh (2025). "Bayesian Hybrid Model Search and Averaging for Sparse Gaussian Process Regression", Statistical Analysis and Data Mining: The ASA Data Science Journal, 18(2), e70018.

M. Hua & G. Goh (2025). "Posterior model consistency in high-dimensional Bayesian variable selection with arbitrary priors", Statistics and Probability Letters, 223, 110415.