| name: Henry Scharf | office: GMCS 518 or |
| email: hscharf@sdsu.edu | office hours: T/Th 10:00-11:00am or by appt. |
| (please let me know you’re coming) |
| course number: STAT 676 | semester: Spring 2022 |
| meeting times: T/Th 12:30–1:45pm | room: GMCS 327 ( back up room) |
| mode of delivery: in-person lecture | platform: Canvas, Gradescope |
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| prerequisites: Statistics 551B or 670B. Some programming experience with R will be helpful. | |
If for some reason we cannot meet in person as a class, we will adopt a synchronous, virtual schedule. Classes will happen at the same scheduled time, but we’ll utilize this Zoom room (link will also be available through Canvas on our homepage and calendar).
For millennia, the Kumeyaay people have been a part of this land. This land has nourished, healed, protected and embraced them for many generations in a relationship of balance and harmony. As members of the San Diego State University community, we acknowledge this legacy. We promote this balance and harmony. We find inspiration from this land, the land of the Kumeyaay.
“It is unanimously agreed that statistics depends somehow on probability. But, as to what probability is and how it is connected with statistics, there has seldom been such complete disagreement and breakdown of communication since the Tower of Babel. Doubtless, much of the disagreement is merely terminological and would disappear under sufficiently sharp analysis.” –Leonard J. Savage, The Foundations of Statistics (1972)
This course will focus on fundamental theoretical and applied concepts in Bayesian statistics including: Bayes’ theorem; conjugate priors; likelihood principle; posterior probability intervals; prior elicitation; computational techniques; hierarchical models; posterior sensitivity analysis; decision theory.
Students will be able to:
In-class quizzes (15%): A very short quiz will be given during the first 5-10 minutes of each class meeting. The lowest two scores will be dropped. All quizzes will contribute equally.
Homework (35%): Homework assignments will be given every 2-3 weeks in the form of an Rlab (see schedule). Each assignment will begin in class (virtually), and students will have 8 days to submit their solutions online.
Independent research project (IRP) (25%): Each student will prepare a short exposition in the form of a pre-recorded presentation or written vignette on a topic of their choosing relevant to Bayesian statistics. Topics could be extensions/in-depth presentations of concepts covered in class, applications of Bayesian statistics in the news or scientific literature, etc. Presentations should be aimed at an audience of peers (i.e., classmates). Pair-based IRPs may be allowed; talk to me first.
Final project (25%): Groups will be made up of 3-4 students chosen by me. Each group will identify one or more real data sets and perform an analysis using Bayesian statistical methods from this course (and beyond, if desired).
I try to grade assignments as quickly as I can because I think it is most useful for students to receive feedback as soon as possible. Grades and feedback will be posted on Canvas. If you have a question about grades or notice an inaccuracy, please let me know right away.
Letter grade: Students earning final grades in the following ranges will receive the corresponding letter grade or higher (square brackets are inclusive, round parentheses are not).
| Percent | Letter | Grade Point |
|---|---|---|
| [90, 100] | A | 4.0 |
| [80, 90) | B | 3.0 |
| [70, 80) | C | 2.0 |
| [60, 70) | D | 1.0 |
| [0, 60) | F | 0 |
Late Policy: Brief extensions will be granted for assignments when a reasonable request if made at least 48 hours before the due date. If no arrangements have been made in advance, a late penalty of 25% of the total assignment grade per day will be assessed.
Academic Honesty: The University adheres to a strict policy prohibiting cheating and plagiarism including:
The California State University system requires instructors to report all instances of academic misconduct to the Center for Student Rights and Responsibilities. Academic dishonesty will result in disciplinary review by the University and may lead to probation, suspension, or expulsion.
A First Course in Bayesian Statistical Methods [Hoff] by Peter D. Hoff
Available through SDSU bookstore or online.
Bayesian Data Analysis [BDA] by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, and Donald B. Rubin
A thorough, practical reference for implementing Bayesian statistics. A very important text and constant reference for many Bayesians, including myself.
Bringing Bayesian Models to Life by Mevin Hooten and Trevor Hefley
A practical guide to implementing Bayesian models using R (by Henry’s former PhD advisor).
Computational Statistics by Geof Givens and Jennifer Hoeting
A great computational statistics reference with chapters dedicated to Bayesian methods, especially MCMC.
Available here, subject to change.
Motivation to participate in this class needs to come primarily from within. Some of the assessment structures provide minimal external nudging to keep students going (e.g., quizzes), but for the most part your success will be a product of your own internal desire to actually learn this stuff. For my part as the instructor, this means I have planned zero in-class exams, and will try to keep topics as immediately relevant for you, the students, as possible. I will try to be responsive to all your requests throughout the semester. If you find something boring/useless, I’ll try to take it out. If you want me to go into more depth on a particular topic, I’ll try to make time to do that.
For your part as a student, this means you will have to manage your own time carefully and do whatever you must to make assignments/projects relevant for you. When there is an opportunity, find data sets you care about and want to analyze. Focus on methods you want to be able to take with you throughout your career. A fully engaged student will probably find that she is frequently searching online for more information about something we discussed in class. He may find himself listening to unassigned podcasts and reading blogs written by experts. From time to time, they may bump up against a problem to which the collective response of humanity is “We don’t know how to do that…yet.”
I am committed to ensuring each student’s access to all course materials, time and attention from me as the instructor both in and out of class, and fair opportunities to demonstrate mastery of the course content. Please contact me if you require any special assistance or accommodations. To avoid any delay in the receipt of your accommodations, you should contact Student Ability Success Center as soon as possible. Please note that accommodations are not retroactive, and that I cannot provide accommodations based upon disability until I have received an accommodation letter from Student Ability Success Center.
I encourage students to reach out by email anytime they need help or have a question. I endeavor to respond to all emails within 24 hours during the work week. Generally I will not be able to respond during the weekend. For questions that require a longer response than a few sentences, please visit me during office hours or schedule a meeting with me. For questions that can be easily answered through a straightforward search online, you may receive a terse reply inviting you to find the answer on your own. (For example: STUDENT: When are your office hours again? ME: You can figure that out without my help. I believe in you!)
Religious observances: In accordance with the University Policy File, please notify me about planned absences for religious observances by the end of the second week of classes.
Medical-related absences: Please contact me if you need to miss class, etc. due to an illness, injury or emergency. For the purposes of addressing university policy, documentation may be requested.
SDSU Economic Crisis Response Team: If you or a friend are experiencing food or housing insecurity, or any unforeseen financial crisis, visit sdsu.edu/ecrt, email ecrt@sdsu.edu, or walk-in to Well-being & Health Promotion on the 3rd floor of Calpulli Center.
The Art of R Programming by Norman Matloff
R for Data Science by Hadley Wickham and Garrett Grolemund. Especially for those who live in, or are curious about, the tidyverse.
Fundamentals of Data Visualization by Claus O. Wilke
There are many other useful references online. If you find a particularly good one you think others would appreciate, please let us know!