Bayesian parametric and nonparametric modeling
Flexible models and prior structures for regression, density estimation, covariance modeling, and structured uncertainty.
Department of Statistics and Data Science.
Bayesian Inference LAB
Welcome to the Posterior Space
Research Areas
Bayesian inference for uncertainty-aware data science
Our lab develops Bayesian methodology for principled uncertainty quantification, scalable posterior inference, and statistical learning with complex data.
Bayesian computation and posterior approximation
Monte Carlo methods, variational approximation, Gaussian process computation, and scalable inference algorithms.
Kernel methods and probabilistic machine learning
Bayesian kernel machines, Gaussian process models, and uncertainty-aware prediction for high-dimensional and structured data.
Scientific and applied data analysis
Applications in biomedical studies, sensor data, environmental risk, aquaculture, molecular toxicity, and industrial data science.
Software
Research code, packages, and reproducible tools from the lab will be collected on the software page.
Join the lab
We welcome students interested in Bayesian inference, probabilistic machine learning, and scientific AI.
Emailstatjs@inha.ac.kr
Office5N-435B
AddressDepartment of Statistics and Data Science, INHA University
100 Inha-ro, Michuhol-gu, Incheon 22212, Korea
HomepageProfessor Seongil Jo