Spring 2022 - STAT 460 D100
Bayesian Statistics (3)
Class Number: 6907
Delivery Method: In Person
Overview
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Course Times + Location:
Jan 10 – Apr 11, 2022: Tue, 10:30 a.m.–12:20 p.m.
BurnabyJan 10 – Apr 11, 2022: Fri, 10:30–11:20 a.m.
Burnaby -
Exam Times + Location:
Apr 14, 2022
Thu, 7:00–10:00 p.m.
Burnaby
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Instructor:
David Stenning
dstennin@sfu.ca
778.782.7461
Office: SCK 10575
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Prerequisites:
STAT 330 and 350, with a minimum grade of C-.
Description
CALENDAR DESCRIPTION:
The Bayesian approach to statistics is an alternative and increasingly popular way of quantifying uncertainty in the presence of data. This course considers comparative statistical inference, prior distributions, Bayesian computation, and applications. Quantitative.
COURSE DETAILS:
Course Outline:
1. The basics:
the Bayesian paradigm
comparative statistical inference
2. Priors:
conjugate priors
prior elicitation
reference priors
improper priors
discrete mass priors
3. Computations:
quadrature
importance sampling
Markov chain Monte Carlo
4. Other topics as time permits:
testing via Bayes factors
interval and point estimation
elementary decision theory
hierarchical models
Dirichlet process
5. Applications
Grading
- Assignments 25%
- Midterm 25%
- Final Exam 50%
NOTES:
Above grading is subject to change.
REQUIREMENTS:
Sufficient knowledge of R to code Bayesian computing algorithms.
Materials
REQUIRED READING:
Bayesian Data Analysis, 3rd Edition. Authors: Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin
Book is available online for free http://www.stat.columbia.edu/~gelman/book/BDA3.pdf
Department Undergraduate Notes:
Tutor Requests:
Students looking for a tutor should visit https://www.sfu.ca/stat-actsci/all-students/other-resources/tutoring.html. We accept no responsibility for the consequences of any actions taken related to tutors.
Registrar Notes:
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity web site http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
TEACHING AT SFU IN SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (caladmin@sfu.ca or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.