- Code STAT7016
- Unit Value 6 units
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.
Upon successful completion, students will have the knowledge and skills to:Upon successful completion of the requirements for this course, students should have the
knowledge and skills to:
1. Explain in detail the Bayesian framework for data analysis and its flexibility and be
able to demonstrate when the Bayesian approach can be beneficial.
2. Develop, analytically describe, and implement both single and multiparameter
probability models in the Bayesian framework.
3. Demonstrate the role of the prior distribution in Bayesian inference and be able to
articulate the usage of non-informative priors and conjugate priors.
4. Show high level Interpretation of Bayesian Analysis Results and be able to readily
perform Bayesian model evaluation and assessment.
5. Demonstrate the necessary skills to: fit hierarchical models, provide thorough
technical specifications for these models.
6. Perform Bayesian computation using Markov chain Monte Carlo methods using R
7. Demonstrate how Bayesian Methods can be used to solve real world problems.
8. Communicate complex statistical ideas to a diverse audience.
9. Demonstrate the necessary research skills to form a hypothesis, collect and analyse
data, and reach appropriate conclusions.
Indicative AssessmentTypical assessment may include, but is not restricted to: assignments and a final project.
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Students are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.
Requisite and Incompatibility
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:
- 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.
Offerings and Dates
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|8375||22 Jul 2019||29 Jul 2019||31 Aug 2019||25 Oct 2019||In Person||N/A|