国产精品
Science Mathematics and Statistics

Bayesian Inference and Computation (MATH3871)

Overview

MATH3871 is a 3rd year course.聽

Units of credit:听6

Prerequisites:聽聽MATH2801 or MATH2901

Equivalent courses:聽MATH5960 (jointly taught)

Cycle of offering:聽Term 3

Graduate attributes:聽The course will enhance your research, inquiry and analytical thinking abilities.

More information:聽The course handout contains information about course objectives, assessment, course materials and the syllabus.

Important additional information as of 2023

国产精品 Plagiarism Policy

The University requires all students to be aware of its聽.

For courses convened by the聽School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.

If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.

罢丑别听聽contains information about the course. (The timetable is only up-to-date if the course is being offered this year.)

If you are currently enrolled in MATH3871, you can log into聽聽for this course.

Course aims

This course aims to:

  • Provide a strong background in the concepts and philosophy of Bayesian inference;
  • Instill an appreciation of the benefits of the Bayesian framework;
  • Provide extensive practical opportunities to implement Bayesian data analyses;
  • Present an overview of research activity in this field.

Course description

After describing the fundamentals of Bayesian inference, this course will examine the specification of prior and posterior distributions, Bayesian decision theoretic concepts, the ideas behind Bayesian hypothesis tests, model choice and model averaging, and evaluate the聽 capabilities of several common model types, such as hierarchical and mixture models. An important part of Bayesian inference is the requirement to numerically evaluate complex integrals on a routine basis. Accordingly this course will also introduce the ideas behind Monte Carlo integration, importance sampling, rejection sampling, Markov chain Monte Carlo samplers such as the Gibbs sampler and the Metropolis-Hastings algorithm, and use of the WinBuGS posterior simulation software.