Spring 2019 - STAT 460 D100

Bayesian Statistics (3)

Class Number: 3452

Delivery Method: In Person

Overview

  • Course Times + Location:

    Tu 11:30 AM – 1:20 PM
    WMC 2533, Burnaby

    Th 11:30 AM – 12:20 PM
    WMC 3220, Burnaby

  • Exam Times + Location:

    Apr 16, 2019
    12:00 PM – 3:00 PM
    AQ 5037, Burnaby

  • Prerequisites:

    STAT 330 and 350.

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

  • Participation 15%
  • Assignments 20%
  • Midterm 20%
  • Final Exam 45%

NOTES:

Above grading is subject to change.

Materials

RECOMMENDED READING:

-Bayes and Empirical Bayes Methdos for Data Analysis (Carlin &Louis)
-Bayesian Data Analysis (Gelman, Carlin, Stern & Rubin)

Department Undergraduate Notes:

Students with Disabilites:
Students requiring accommodations as a result of disability must contact the Centre for Accessible Learning 778-782-3112 or csdo@sfu.ca


Tutor Requests:
Students looking for a Tutor should visit http://www.stat.sfu.ca/teaching/need-a-tutor-.html. We accept no responsibility for the consequences of any actions taken related to tutors.

Registrar Notes:

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

ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS