This course is about computational techniques used in statistical inference. Topics will be selected from: computer arithmetic, Monte Carlo methods for statistical research, optimization methods for maximum likelihood estimation, numerical methods for Bayesian inference, Bayesian analysis using BUGS, bootstrap methods, matrix computations for linear models, and others. This course also serves as a tutorial on a statistical programming language, such as R or Matlab, with examples from statistical inference. Weekly hours: 3 Lecture hoursPrerequisite(s): STAT 342, STAT 344, and STAT 442 or by permission of the instructor. Note: Students with credit for STAT 846: Special Topics in Probability and Statistics; Special Topics in Computational Techniques in Statistics; and Special Topics in Computational Statistics may not take this course for credit.
This course is about computational techniques used in statistical inference. Topics will be selected from: computer arithmetic, Monte Carlo methods for statistical research, optimization methods for maximum likelihood estimation, numerical methods for Bayesian inference, Bayesian analysis using BUGS, bootstrap methods, matrix computations for linear models, and others. This course also serves as a tutorial on a statistical programming language, such as R or Matlab, with examples from statistical inference. Weekly hours: 3 Lecture hoursPrerequisite(s): STAT 342, STAT 344, and STAT 442 or by permission of the instructor. Note: Students with credit for STAT 846: Special Topics in Probability and Statistics; Special Topics in Computational Techniques in Statistics; and Special Topics in Computational Statistics may not take this course for credit.
