- Code STAT3016
- 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:
- Explain the Bayesian framework for data analysis and when it can be beneficial, including its flexibility in contrast to the frequentist approach.
- Develop, analytically describe, and implement complex single and multiparameter probability models in the Bayesian framework.
- 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.
- Interpret the results of a Bayesian analysis and perform Bayesian model evaluation and assessment.
- 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, including forming a hypothesis, collecting and analysing data, and reaching appropriate conclusions.
- 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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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.
Requisite and Incompatibility
Information about the prescribed textbook will be available via the Class Summary.
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you 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). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
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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|
|6984||24 Jul 2023||31 Jul 2023||31 Aug 2023||27 Oct 2023||In Person||N/A|