• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Course subject Statistics

The Bayesian approach to statistics assigns probability distributions to both the data and unknown parameters in the problem.  This way, we can incorporate prior knowledge on the unknown parameters before observing any data.  Statistical inference is summarised by the posterior distribution of the parameters after data collection, and posterior predictions for new observations.  The Bayesian approach to statistics is very flexible because we can describe the probability distribution of any function of the unknown parameters in the model.  Modern advances in computing have allowed many complicated models, which are difficult to analyse using ‘classical’ (frequentist) methods, to be readily analysed using Bayesian methodology.  

The aim of this course is to equip students with the skills to perform and interpret Bayesian statistical analyses.  The first part of the course is devoted to describing the fundamentals of Bayesian inference by examining some simple Bayesian models.  More complicated models will then be explored, including linear regression and hierarchical models in a Bayesian framework.  Bayesian computational methods, especially Markov Chain Monte Carlo methods will progressively be introduced as motivated by the models discussed.   Emphasis will also be placed on model checking and evaluation.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Explain the Bayesian framework for data analysis and when it can be beneficial, including its flexibility in contrast to the frequentist approach;
  2. Develop, analytically describe, and implement complex single and multiparameter probability models in the Bayesian framework;
  3. Demonstrate an understanding of the role of the prior distribution in Bayesian inference, and in particular the usage of non-informative priors and conjugate priors;
  4. Interpret the results of a Bayesian analysis and perform Bayesian model evaluation and assessment;
  5. Fit hierarchical models and provide the technical specifications for such models;
  6. Perform Bayesian computation using Markov chain Monte Carlo methods using R; and,
  7. Formulate a Bayesian solution to real-data problems, including forming a hypothesis, collecting and analysing data, and reaching appropriate conclusions.

Indicative Assessment

  1. Typical assessment may include, but is not restricted to: exams, assignments, quizzes, presentations and other assessment as appropriate (100) [LO 1,2,3,4,5,6,7]

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Workload

Students are expected to commit 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must have completed STAT2008 or STAT2014 and have completed or be concurrently enrolled in STAT2001 or STAT2013.

Prescribed Texts

Information about the prescribed textbook will be available via the Class Summary.

Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
2
Unit value:
6 units

If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees.  Where there is a unit range displayed for this course, not all unit options below may be available.

Units EFTSL
6.00 0.12500
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7887 27 Jul 2020 03 Aug 2020 31 Aug 2020 30 Oct 2020 In Person N/A

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