Time: Monday, Wednesday, Friday at 11:00 AM - 11:50 AM
Location: Location (in person and on campus): Burnham Hall (828 S Halsted St, Chicago, IL 60607), Room B6
Instructor: Jie Yang
Office: SEO 513
Phone: (312) 413-3748
E-Mail: jyang06 AT uic DOT edu
Office Hours: Monday, Wednesday, Friday at 10:00 a.m. - 10:50 a.m. at UIC Zoom or by appointment
Textbook (required): Geof H. Givens and
Jennifer A. Hoeting,
Computational Statistics,
John Wiley & Sons, Inc., 2nd edition, 2013.
Preview table of contents and preface.
Course Description: STAT 485. Computational Statistics.
Modern computationally-intensive statistical methods including Monte Carlo integration and simulation, optimization and maximum likelihood estimation, EM algorithm, MCMC, sampling and resampling methods, non-parametric density estimation.
Course Credits: 3 hours for undergraduates or 4 hours for graduate students.
Prerequisite: STAT 411 or consent of instructor.
Course Goals: Understand modern computationally-intensive statistical methods; formulate and implement computational techniques by using standard statistical software; apply statistical computing techniques to real life applications and research projects.
Learning Objectives: Understand and implement Monte Carlo integration and simulation; implement maximum likelihood estimation, EM algorithm, MCMC, sampling and resampling methods; apply non-parametric density estimation to real applications.
Attendance/Participation Policy:
Students are expected to attend the lectures and participate in the discussions. Attendance will be counted at least six times during the course period and the students who present may receive half credit point each time and up to three extra credit points in total on the final grade at a 100-point scale. Students who actively participate in the discussion may receive half or one credit point each time and up to five credit points in total. If for any reason you could not present in class on time, please send me an email at your earliest convenience. If you need special accommodations due to disabilities, please contact the Disability Resource Center for a Letter of Accommodation (LOA).
Assignments, Due Dates, and Deadlines:
Homework will be assigned at a weekly basis;
turn in your homework every Friday before class via UIC Blackboard;
half of the grade counts for completeness;
half of the grade counts for correctness of one selected problem.
Policy for Missed or Late Homework: Students may request up to three days' extension for each homework; late homework without request ahead of time or longer than three days will not be accepted; the lowest one homework score will be dropped for final grade.
Exam: April 8th, 2026 (Wednesday), 11:00 a.m. - 11:50 a.m.
Project: Students are required to work in groups on course projects and submit their final reports before May 1st, 2026, Friday, 11:00am.
Each group should consist of at most three students. The projects may come from the optional problems assigned by the instructor or be proposed by the students themselves upon the approval of the instructor.
Grading: Homework 30%, Exam 30%, Project 40%
Grading Scale: 90% A , 80% B , 70% C , 60% D
Format of Exam: The exam is based on the homework and the examples discussed in class. The last class session before the exam is a review session. Please prepare any questions that you may have.
No makeup exam will be given without a valid excuse.
| WEEK | SECTIONS | BRIEF DESCRIPTION |
| 01/12 - 01/16 | Introduction; 6.1; 6.2 | Introduction to the Monte Carlo method; Exact simulation |
| 01/19 - 01/23 | Holiday; 6.2; 6.3 | Exact simulation; Approximate simulation |
| 01/26 - 01/30 | 6.3; 6.4; 6.4 | Approximate simulation; Variance reduction techniques |
| 02/02 - 02/06 | 1.7; 7.1; 7.1 | Markov chains; Metropolis-Hastings algorithm |
| 02/09 - 02/13 | 7.2; 7.2; 7.3 | Gibbs sampling; Implementation |
| 02/16 - 02/20 | 9.1; 9.2; 9.2 | The bootstrap principle; Basic methods |
| 02/23 - 02/27 | 9.2; 9.3; 9.3 | Basic methods; Bootstrap inference |
| 03/02 - 03/06 | 9.8; 4.1; 4.1 | Permutation tests; Missing data, marginalization, and notation |
| 03/09 - 03/13 | 4.2; 4.2; 4.2 | The EM algorithm |
| 03/16 - 03/20 | 4.3; 4.3; 10.1 | EM Variants; Measures of performance |
| 03/30 - 04/03 | 10.2; 10.2; 10.3 | Kernel density estimation; Nonkernel methods |
| 04/06 - 04/10 | Review; Exam; 11.1 | Predictor-response data |
| 04/13 - 04/17 | 11.1; 11.2; 11.2 | Predictor-response data; Linear smoothers |
| 04/20 - 04/24 | 11.3; 11.3; 11.4 | Comparison of linear smoothers; Nonlinear smoothers |
| 04/27 - 05/01 | 11.4; 11.5; 11.5 | Nonlinear smoothers; Confidence bands |