Bei Jiang, PhD
Faculty of Science - Mathematics & Statistical Sciences
Contact
Professor
Faculty of Science - Mathematics & Statistical Sciences
- bei1@ualberta.ca
Overview
Research
Research Areas
Methods for Joint Modeling of Longitudinal and Health Outcome Data, Bayesian Hierarchical Modeling, Mixture Modeling, Functional and Imaging Data Analysis, Kernel Machine Regression/Classification, Bayesian Support Vector Machine.
Courses
STAT 541 - Statistics for Learning
The course focuses on statistical learning techniques, in particular those of supervised classification, both from statistical (logistic regression, discriminant analysis, nearest neighbours, and others) and machine learning background (tree-based methods, neural networks, support vector machines), with the emphasis on decision-theoretic underpinnings and other statistical aspects, flexible model building (regularization with penalties), and algorithmic solutions. Selected methods of unsupervised classification (clustering) and some related regression methods are covered as well. Prerequisite: Consent of the instructor.
STAT 575 - Multivariate Analysis
The multivariate normal distribution, multivariate regression and analysis of variance, classification, canonical correlation, principal components, factor analysis. Prerequisite: STAT 372 and STAT 512.