Spring 2022 - STAT 460 D100

Bayesian Statistics (3)

Class Number: 6907

Delivery Method: In Person

Overview

  • Course Times + Location:

    Tu 10:30 AM – 12:20 PM
    AQ 5018, Burnaby

    Fr 10:30 AM – 11:20 AM
    AQ 5016, Burnaby

  • 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.