Simon Fraser University
Engaging the World

Course Outlines

Fall 2025 - STAT 460 D100

Bayesian Statistics (3)

Class Number: 7136

Delivery Method: In Person

Overview

  • Course Times + Location:

    Sep 3 – Dec 2, 2025: Tue, 10:30 a.m.–12:20 p.m.
    Burnaby

    Sep 3 – Dec 2, 2025: Fri, 10:30–11:20 a.m.
    Burnaby

  • Exam Times + Location:

    Dec 15, 2025
    Mon, 3:30–6:30 p.m.
    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:

Part 1: Introduction to Bayesian Statistics

  • Motivation for Bayesian Analysis
  • Review of Likelihood-Based Inference
  • Comparison of Likelihood-Based and Bayesian Inference

Part 2: Bayesian Inference for Simple Models

  • Bayesian Analysis in a Nutshell
  • Informative Prior distributions
  • Binomial Models
  • Poisson Models
  • Univariate Normal Models
  • Summarizing Posterior Inference
  • Noninformative and Weakly Informative Prior Distributions
  • Scientific Application: Hardness Ratios in High-Energy Astrophysics

Part 3: Multiparameter Models

  • Marginalization and Monte Carlo
  • Normal Data with Noninformative and Conjugate Priors
  • Scientific Application: Estimating the Speed of Light
  • Multinomial Models
  • Scientific Application: Analysis of a Bioassay Experiment

Part 4: Bayesian Computing

  • Introduction to Bayesian Computing
  • Rejection Sampling
  • Importance Sampling
  • Introduction to Markov Chain Monte Carlo
  • Metropolis and Metropolis-Hastings Algorithms
  • Gibbs Sampler
  • Hybrid and Alternative Algorithms
  • Scientific Application: Inference in High-Energy Astrophysics

Part 5 (time permitting):

  • Bayesian Hierarchical Models
  • Hyperpriors
  • James-Stein Estimators and Shrinkage
  • Scientific Application: Supernova Color Corrections

Grading

  • Assignments 20%
  • Midterm 1 20%
  • Midterm 2 20%
  • Final Exam 40%

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

REQUIRED READING NOTES:

Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.

Department Undergraduate Notes:

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


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

At SFU, you are expected to act honestly and responsibly in all your academic work. Cheating, plagiarism, or any other form of academic dishonesty harms your own learning, undermines the efforts of your classmates who pursue their studies honestly, and goes against the core values of the university.

To learn more about the academic disciplinary process and relevant academic supports, visit: 


RELIGIOUS ACCOMMODATION

Students with a faith background who may need accommodations during the term are encouraged to assess their needs as soon as possible and review the Multifaith religious accommodations website. The page outlines ways they begin working toward an accommodation and ensure solutions can be reached in a timely fashion.