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:
- Explain the Bayesian framework for data analysis and its flexibility in contrast to the frequentist approach; appreciate when the Bayesian approach can be beneficial.
- Develop, analytically describe, and implement common probability models (both single and multiparameter) in the Bayesian framework (this includes models for regression analysis and generalised linear models).
- Appreciate the role of the prior distribution in Bayesian inference, and in particular the usage of non-informative priors and conjugate priors.
- Interpret the results of a Bayesian analysis and perform Bayesian model evaluation and assessment.
- Recognise the need to fit hierarchical models and provide the technical specifications for such models.
- Perform Bayesian computation using Markov chain Monte Carlo methods using R.
- Formulate a Bayesian solution to real-data problems.
- Communicate complex statistical ideas to a diverse audience.
- 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.
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- 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, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|8721||24 Jul 2017||31 Jul 2017||31 Aug 2017||27 Oct 2017||In Person||N/A|