• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
• ANU College ANU College of Business and Economics
• Course subject Statistics
• Course convener
• Dr Bronwyn Loong
• Mode of delivery In Person
• Co-taught Course
• Offered in Second Semester 2020
Introduction to Bayesian Data Analysis (STAT7016)

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 in detail 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 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 perform Bayesian model evaluation and assessment;
5. Fit hierarchical models and provide thorough technical specifications for these models;
6. Perform Bayesian computation using Markov chain Monte Carlo methods using R; and,
7. Demonstrate how Bayesian Methods can be used to solve real world 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]

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

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.

Not applicable

## Requisite and Incompatibility

To enrol in this course you must have completed STAT6039 or STAT6013, and have completed STAT6038 or STAT6014 or STAT7001. Incompatible with STAT3016 and STAT4116.

## 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

## Course fees

Domestic fee paying students
Year Fee
2020 \$4050
International fee paying students
Year Fee
2020 \$5760
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
7888 27 Jul 2020 03 Aug 2020 31 Aug 2020 30 Oct 2020 In Person N/A