Courses Taught by Jian Kang

BIOSTAT815: Advanced Topics in Computational Statistics

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Kang, Jian (Residential);
  • Prerequisites: BIOSTAT601, BIOSTAT602 and BIOSTAT615 or equiv and proficiency in C++ and R
  • Description: Modern numerical analysis for statisticians. Combination of theory and practical computational examples illustrating the current trends in numerical analysis relevant to probability and statistics. Topics choose from numerical linear algebra, optimization theory, quadrature methods, splines, and Markov chains. Emphasis on newer techniques such as quasi-random methods of integration, the EM algorithm and its variants, and hidden Markov chains. Applications as time permits to areas such as genetic and medical imaging.
  • Syllabus for BIOSTAT815

BIOSTAT882: Advanced Bayesian Inference

  • Graduate level
  • Residential
  • Winter term(s) for residential students;
  • 3 credit hour(s) for residential students;
  • Instructor(s): Kang, Jian (Residential);
  • Prerequisites: N/A
  • Advisory Prerequisites: Biostatistics 682 or an equivalent course covering the basic Bayesian methods and theory. Previous experience in programming in R or C/C++ is required.
  • Description: This course focuses on advanced Bayesian theory and nonparametric Bayes methods including Gaussian processes, Dirichlet processes, deep neural networks, variable selection, and shrinkage priors, along with modern posterior computation algorithms including gradient based Markov chain Monte Carlo and variational Bayesian methods.
  • Learning Objectives: This course focuses on the advanced Bayesian inference methods including modeling, theory and computation. The target audience is the PhD candidates in Biostatistics who are interested in working on their research topics related to Bayesian statistics. R and C++ will be used for illustrations and practices.