Course Schedule - Spring Semester 2022


STAT 635 901 (CRN: 25780)


Department: Statistics
Instructor: Li, Meng
Meeting: 2:00PM - 3:15PM MW MXF 251 (10-JAN-2022 - 22-APR-2022) 
Session: Full Term
Grade Mode: Standard Letter
Course Type: Lecture
Language of Instruction: Taught in English
Method of Instruction: Online
Credit Hours: 3
Course Syllabus:
Course Materials: Rice Campus Store
Must be enrolled in one of the following Level(s):
Section Max Enrollment: 49
Section Enrolled: 5
Enrollment data as of: 23-FEB-2024 11:01AM
Additional Fees: None
Final Exam: GR Course-Dept Schedules Exam
Description: Statistical inference is the de facto tool in data science to carry out hypothesis testing and draw conclusions under uncertainty. With increasingly diverse stakeholders relying on inference as data-driven solutions, to study, decipher, and articulate its strength and limitation become more important than ever. In this course, we will discuss fundamental issues in statistical inference, partly in response to a range of daunting challenges posed by modern data science such as reproducibility and interpretability at large scales. A sample of topics includes the use of p-values vs. Bayes factors, frequentist properties of Bayesian procedures for both parametric and nonparametric models, Bernstein von-Mises phenomena, variable/feature selection, post-selection inference, false discovery control. Recommended Prerequisite(s): STAT 532/533 or equivalent courses on classical statistical inference, and STAT525 or equivalent courses on Bayesian inference.