About Xiaotian Jin Xiaotian Jin Ph.D. Student, Statistics Statistical Modeling spatio-temporal statistics scientific computing Python (Programming Language) statistical analysis algorithms optimization Xiaotian's research focuses on developing computationally efficient non-Gaussian statistical frameworks and the theoretical analysis, with a particular focus on modeling complex spatio-temporal dependencies in high-dimensional environmental datasets. Events Presented Events Apr 26 - May 2, 2026 A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework Xiaotian Jin, Ph.D. Student, Statistics Apr 29, 15:00 - 16:30 B4 R5209 latent Gaussian models spatio-temporal statistics stochastic algorithms multivariate statistics This thesis develops a linear latent non-Gaussian modeling framework that extends latent Gaussian models to accommodate skewness, heavy tails, and extremes while preserving computational tractability, along with its implementation in the R package ngme2.
A Unified and Computationally Efficient Non-Gaussian Statistical Modeling Framework Xiaotian Jin, Ph.D. Student, Statistics Apr 29, 15:00 - 16:30 B4 R5209 latent Gaussian models spatio-temporal statistics stochastic algorithms multivariate statistics This thesis develops a linear latent non-Gaussian modeling framework that extends latent Gaussian models to accommodate skewness, heavy tails, and extremes while preserving computational tractability, along with its implementation in the R package ngme2.
Engage LinkedIn KAUST Repository ORCID GitHub ShareClipboard Related Sites Stochastic Processes and Mathematical Statistics (StochProc) Statistics (STAT) Related Content Articles 1 Events 1
