- Instructor: Jordan Bryan (jbryan@virginia.edu)
- Lecture: Tues, Thurs 9:30 - 10:45 am, Data Science Building Room 246
- Office hours: TBD
- Teaching Assistant: Elizabeth Miller (zrc3hc@virginia.edu)
- Office hours: TBD
- Canvas site
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Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (CASI)
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Statistical Inference (Casella & Berger)
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Course notes
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Course code snippets
- Foundations of inference
- Sampling distributions
- Point estimation, interval estimation, and hypothesis testing
- Optimality criteria
- Likelihood methods
- Parametric models and exponential families
- Information
- Maximum likelihood
- Asymptotic evaluations
- Consistency
- Delta method
- Asymptotic properties of MLEs
- Bootstrapping and resampling methods
- The jackknife estimate of standard error
- Non-parametric bootstrap
- Parametric bootstrap
- Prediction
- Estimates of predictive accuracy
- Shrinkage and ridge regression
- Contemporary ideas in prediction (if time allows)
Jan 13 2026: Course overview
[//]: # Jan 15 2026: TBD HW1 assigned
[//]: # Jan 20 2026: TBD
[//]: # Jan 22 2026: TBD HW2 assigned
[//]: # Jan 27 2026: TBD
[//]: # Jan 29 2026: TBD HW3 assigned
[//]: # Feb 03 2026: TBD
[//]: # Feb 05 2026: TBD HW4 assigned
[//]: # Feb 10 2026: TBD
[//]: # Feb 12 2026: TBD HW5 assigned
[//]: # Feb 17 2026: TBD
[//]: # Feb 19 2026: TBD Midterm practice given
[//]: # Feb 24 2026: Midterm review
Feb 26 2026: Midterm exam
Mar 03-05 2026: Spring recess (no class)
[//]: # Mar 10 2026: TBD
[//]: # Mar 12 2026: TBD HW6 assigned
[//]: # Mar 17 2026: TBD
[//]: # Mar 19 2026: TBD HW7 assigned
[//]: # Mar 24 2026: TBD
[//]: # Mar 26 2026: TBD HW8 assigned
[//]: # Mar 31 2026: TBD
[//]: # Apr 02 2026: TBD HW9 assigned
[//]: # Apr 07 2026: TBD
[//]: # Apr 09 2026: TBD HW10 assigned
[//]: # Apr 14 2026: TBD
[//]: # Apr 16 2026: TBD HW11 assigned
[//]: # Apr 21 2026: TBD
[//]: # Apr 23 2026: TBD
Apr 28 2026: Final review
May 04 2026: Final exam (2:00 - 5:00 pm, location TBD)
Final grades will be computed using the following weighting:
- Attendance (5%)
- Homework (30%)
- Homework explications (15%)
- Midterm exam (20%)
- Final exam (30%)
Grading scale:
- 93-100 A
- 90-92 A-
- 87-89 B+
- 83-86 B
- 80-82 B-
- 77-79 C+
- 73-76 C
- 70-72 C-
- <70 F
Note that a B- is the lowest satisfactory grade for graduate credit.
Submitting Homework
Homework will be accepted through the Assignments page on Canvas. Submissions will be in PDF format. You may hand-write and scan problem solutions, or you may use a typesetting software like LaTeX, Markdown, etc. Some homework assignments will involve using code to produce graphical or numerical outputs and will require the use of software. Please compile all materials in a single PDF for submission and make sure that whatever you have written can be clearly read by the grader.
Grades for (on-time) homework will be made visible to students no later than one week after the assignment due date. Grades for late work (see below) will become available as time permits.
Late Work Policy
The expectation in this course is that all assignments will be submitted on time. Submitting your work on time respects the efforts of your instructor and teaching assistant, and it ensures that you are prepared to learn subsequent material.
Assignments turned in after the due date incur a 10% penalty per late day. For example, an assignment due at 9:30 am on Tuesday that is submitted to Canvas at 3:00 pm on Thursday will incur a 30% penalty. If the assignment would have received a 95% had it been returned on time, then the late grade is 65%. Note that weekend days count towards the late penalty.
I will not accept work that is late by more than one week past its due date.
To provide flexibility for weeks in which life circumstances do not permit the completion of your coursework, your lowest homework grade will be dropped.
Class Attendance
Attendance in this class is mandatory. If you need to miss a class for any reason, please email me in advance. You are responsible for keeping up with the lecture material, but I am happy to work with you during office hours or by appointment to brush up on things you may have missed.
Extenuating Circumstances
Students are expected to communicate with me as soon as possible regarding extenuating circumstances and how their participation in the course, including attendance and assignment submissions, may be affected by them.