Bayesian Statistical Learning Lab

KYUNGPOOK NATIONAL UNIVERSITY

Welcome to Goh's Bayesian statistical learning lab

Welcome to Bayesian Statistical Learning Lab at Kyungpook National University. We aim to tackle the challenges of modern statistical data analysis through the lens of Bayesian inference, offering innovative solutions to the challenges of big data and high-dimensional datasets.

Join us as we explore the frontiers of Bayesian machine learning, utilizing divergence measures to enhance predictive accuracy and inform decision-making processes. Our lab is a hub for cutting-edge research and collaboration, where curiosity drives discovery and data science meets practical application.

Principal Investigator

Gyuhyeong Goh, Ph.D.

Associate Professor

Department of Statistics, Kyungpook National University

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Publications

28. 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.

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

26. M. Adenauer, M. K. Adjemian, S. Arita, W. Brorsen, J. Cooper, G. Goh, B. Karali, M. L. Mallory, W. Thompson & J. Yu (2025). “Impacts of unilateral U.S. carbon policies on agricultural sector greenhouse gas emissions and commodity markets”, Environmental Research Letters, 20(2), 024022.

25. M. Kim & G. Goh (2024). “A sparse empirical Bayes approach to high-dimensional Gaussian process-based varying coefficient models”, Stat, 13(2), e678.

24. J. Lee & G. Goh (2024). “A hybrid deterministic–deterministic approach for high-dimensional Bayesian variable selection with a default prior”, Computational Statistics, 39, 1659-1681.

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