Biostatistics Courses Taught by Jian Kang

BIOSTAT615: Statistical Computing

  • Graduate level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Kang, Jian
  • Prerequisites: None
  • Description: A survey of key algorithms for statistical computing and its applications in Biostatistics. The course will cover fundamental computational techniques for dynamic programming, sorting, and searching, as well statistical methods for random number generation, numerical integration, function optimization, Markov-Chain Monte Carlo, and the E-M algorithm. Enables students to understand numerical results produced by a computer and to implement their own statistical methods.
  • Syllabus for BIOSTAT615

BIOSTAT682: Applied Bayesian Inference

  • Graduate level
  • Fall term(s)
  • 3 Credit Hour(s)
  • Instructor(s): Kang, Jian
  • Prerequisites: Biostat 602, Biostat 650 and Biostat 651
  • Description: Introduction to Bayesian Inference. Bayesian large sample inference, relationship with maximum likelihood. Choice of model, including prior distribution. Bayesian approaches to regression generalized linear models, categorical data, and hierarchical models. Empirical Bayes methods. Comparison with frequentist methods. Bayesian computational methods. Assessment of sensitivity to model assumptions. Emphasis on biomedical applications.
  • Syllabus for BIOSTAT682