Winter 2022: PSTAT 115: Introduction to Bayesian Data Analysis
In this course, we will explore the data science lifecycle: question formulation, data collection & cleaning, exploratory data analysis & visualization, statistical inference and prediction, and decision-making.
This document and others linked within it should be your PRIMARY source for understanding the expectations of this course. Be sure to read it carefully. You must contact the instructor for clarification if you receive information from any other source that is in contradiction to what is provided below.
Below are the links to different sections of the syllabus:
COURSE INFORMATION
- Instructor: Alex Franks
- Lecture: Monday and Wednesday, 9:30am-10:45pm
- TAs: Jeffrey Wu and Moya Xiong
- ULAs Siming Su, Eric Bletcher and Wenxuan Zhang
Prerequisites:
PSTAT 120B and PSTAT126 or equivalent. All prerequisites with letter grade C or better.
Programming experience: Intermediate familiarity with R and Rmarkdown is required.
Course Topics:
At the end of the course, a successful student will be able to build and refine statistical models using the Bayesian paradigm and utilize Monte Carlo methods for statistical inference. Topics include:
- Review of frequentist inference
- One parameter models
- Monte Carlo computation
- The normal model
- Markov chain Monte Carlo
- Hierarchical models
- An introduction to probabilistic programming
Textbook
- Required Alicia A. Johnson, Miles Q. Ott, Mine Dogucu Bayes Rules https://www.bayesrulesbook.com/ Additional optional resources:
- Optional: Jim Albert Bayesian Computation with R https://www.springer.com/us/book/9780387922973
- Optional: Richard McElreath Statistical Rethinking; https://xcelab.net/rm/statistical-rethinking/
- Optional: Andrew Gelman, John Carlin et al. Bayesian Data Analysis; http://www.stat.columbia.edu/~gelman/book/
Computing Platform
The computing platform (Jupyter Notebooks) for the course is hosted at https://bit.ly/3mSOXtM. All of your work should be completed here. BOOKMARK THIS LINK.
Contact us on Nectir!
All class related questions should be handled through Nectir. Our class page is here: https://ucsb.nectir.io/group/pstat115-w22. An invite link was sent out via Gauchospace.
We ask that when you have a question about the class that might be relevant to other students, post it on Nectir instead of emailing us. That way, all the staff can be on the same page and everyone can benefit from the response.
Support
You are not alone in this course; the mentors (staff and the instructors) are here to support you as you learn the material. It’s expected that some aspects of the course will take time to master, and the best way to master challenging material is to ask questions.
For the first two weeks we will be using Zoom to hold office hours and lab hours. The schedule and links to office hours can be found here. We will update you when we have information about returning in person.
ASSESSMENTS AND GRADES
Your mastery of class material will be assessed in the following ways, and final grades will be computed as follows:
- 5% Class Participation
- 10% Quizzes
- 35% Homework Assignments
- 20% Midterm Exam (Monday February 7, 2022)
- 30% Final Exam (Wednesday, March 16, 2022)
It is certainly possible for all students to receive high grades in this course if all of you show mastery of the homework and lab material.
Participation
Lecture and section attendance is optional but is highly encouraged. You are adults and are responsible for your learning. However, everybody benefits when there is more participation and engagement with the material during lab and lecture.
The participation portion of your grade will also include providing good answers on Piazza and engaging with the various activities that the instructor will provide throughout the quarter.
Assignments
Weekly homework assignments are a required part of the course. You are allowed to work with one partner. When submiting to gradescope, you must include your partners name (link the submission), so that you both get full credit!
Section
- Section attendance is strongly encouraged and can boost your participation grade
- Section content will be uploaded to the Rstudio environment
Exams
We will decide 1-2 weeks in advance whether the exams will be in person or take-home (depending on whether we are still remote). More information will be available later in the course.
Late Policy
Homework will be accepted up to 1 day (24 hours) late; a homework submitted within 24 hours after the deadline will receive a 10 point deduction. No assignments will be accepted 24 hours after the deadline.
If there is a properly documented family emergency, extended illness, documented required court appearance, or other situation beyond the students’ control (with appropriate official detailed documentation) the instructor may extend an assignment deadline, entirely at the instructor’s discretion.
Learning Cooperatively
We encourage you to discuss all of the course activities with your friends and classmates as you are working on them, either on Piazza, or through a personally chat or zoom. Although more difficult this quarter, you will definitely learn more in this class if you work with others than if you do not. Ask questions, answer questions, and share ideas liberally on piazza.
Academic Honesty
Cooperation has a limit. You should not share your code or answers directly with other students. Doing so doesn’t help them; it just sets them up for trouble on exams. Feel free to discuss the problems with others beforehand, but not the solutions. Please complete your own work and keep it to yourself. The exception to this rule is that you can share everything related to a project with your project partner and turn in one project between you.
Penalties for cheating are severe — they range from a zero grade for the assignment up to dismissal from the University, for a second offense.
Rather than copying someone else’s work, ask for help. You are not alone in this course! We are here to help you succeed. If you invest the time to learn the material and complete the projects, you won’t need to copy any answers.
Slides and Recordings
All lecture material including slides will be posted after class on the cloud server. Recordings of the remote lectures will also be posted.
Copyright of Course Materials
My lectures and course materials, including PowerPoint presentations, tests, outlines, and similar materials, are protected by U.S. copyright law and by University policy. I am the exclusive owner of the copyright in those materials I create. You may take notes and make copies of course materials for your own use. You may also share those materials with another student who is enrolled in or auditing this course.
You may not reproduce, distribute or display (post/upload) lecture notes or recordings or course materials in any other way — whether or not a fee is charged — without my express prior written consent. You also may not allow others to do so.
If you do so, you may be subject to student conduct proceedings under the UC Santa Barbara Student Code of Conduct.
Similarly, you own the copyright in your original papers and exam essays. If I am interested in posting your answers or papers on the course web site, I will ask for your written permission.
Disclaimer
The rest of this page details the policies that will be enforced in the Winter 2022 offering of this course. These policies are subject to change throughout the remainder of the course, at the judgement of the course staff.
Last major revision: December 31, 2021