Statistics 210a: Theoretical Statistics
UC Berkeley, Fall 2025
Stat 210A is an introductory Ph.D.-level course in theoretical statistics. It is a fast-paced and demanding course intended to prepare students for research careers in statistics.
Course enrollment for undergraduates
If you are an undergraduate who wants to take this course, please fill out the permission code request form to let me know about your background. If you are unsure of your background, you can check the frequently asked questions regarding preparation and review materials.
Resources
- Syllabus for basic course information
- Gradescope for turning in homework
- EdStem for announcements and technical discussion (no homework spoilers!)
- bCourses for lecture videos and homework solutions
Schedule
Week 1
| Aug 28: | Lecture 1 Introduction | Handwritten notes |
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Week 2
| Sep 2: | Lecture 2 Probability | Handwritten notes |
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| Sep 4: | Lecture 3 Estimation | Handwritten notes |
| Sep 5: | Recitation 1 Probability review |
Week 3
| Sep 9: | Lecture 4 Sufficiency | Handwritten notes |
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| Sep 10: | Homework 1 Problems | LaTeX source |
| Sep 11: | Lecture 5 Exponential Families | Handwritten notes |
Week 4
| Sep 16: | Lecture 6 Exponential Families (2nd lecture) | Handwritten notes |
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| Sep 17: | Homework 2 Problems | LaTeX source |
| Sep 18: | Lecture 7 Completeness | Handwritten notes |
Week 5
| Sep 23: | Lecture 8 Unbiased Estimation | Handwritten notes |
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| Sep 24: | Homework 3 Problems | LaTeX source |
| Sep 25: | Lecture 9 Score and Fisher Information | Handwritten notes |
Week 6
| Sep 30: | Lecture 10 Bayes Estimation | Handwritten notes |
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| Oct 1: | Homework 4 Problems | LaTeX source |
| Oct 2: | Lecture 11 Interpretation and Choice of Prior | Handwritten notes |
Week 7
| Oct 7: | Lecture 12 Bayesian Computation | Handwritten notes |
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| Oct 8: | Homework 5 Problems | LaTeX source |
| Oct 9: | Lecture 13 James--Stein Estimator | Handwritten notes |
Week 8
| Oct 14: | Lecture 14 Minimax Estimation | Handwritten notes |
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| Oct 15: | Homework 6 Problems | Data LaTeX source |
| Oct 16: | Lecture 15 Hypothesis Testing | Handwritten notes |
Week 9
| Oct 21: | Lecture 16 Testing with one parameter | Handwritten notes |
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| Oct 22: | Homework 7 Problems | LaTeX source |
| Oct 23: | Lecture 17 p-values and confidence intervals | Handwritten notes |
Week 10
| Oct 28: | Lecture 18 Testing with nuisance parameters | Handwritten notes |
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| Oct 29: | Homework 8 Problems | LaTeX source |
| Oct 30: | Lecture 19 Testing in linear models | Handwritten notes |
Week 11
| Nov 4: | Lecture 20 Testing in linear models (second lecture) | Handwritten notes |
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| Nov 5: | Homework 9 Problems | LaTeX source |
| Nov 6: | Lecture 21 Asymptotics | Handwritten notes |
Week 12
| Nov 12: | Homework 10 Problems | LaTeX source Data for problem 3 |
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| Nov 13: | Lecture 22 Maximum likelihood estimation | Handwritten notes |
Week 13
| Nov 18: | Lecture 23 MLE Consistency | Handwritten notes |
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| Nov 19: | Homework 11 Problems | LaTeX source |
| Nov 20: | Lecture 24 Likelihood-based Inference | Handwritten notes |
Week 14
| Nov 25: | Lecture 25 Bootstrap | Handwritten notes |
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Week 15
| Dec 2: | Lecture 26 Multiple Testing (Part I) | Handwritten notes |
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| Dec 5: | Homework 12 Problems | LaTeX source |
| Dec 4: | Lecture 26 Multiple Testing (Part II) | Handwritten notes |
Week 17
| Dec 16: | Exam Final Exam, 3-6pm |
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