- Class Number 5835
- Term Code 3260
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Bronwyn Loong
- Dr Bronwyn Loong
- Class Dates
- Class Start Date 25/07/2022
- Class End Date 28/10/2022
- Census Date 31/08/2022
- Last Date to Enrol 01/08/2022
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 in detail 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 both single and multiparameter probability models in the Bayesian framework;
- Demonstrate the role of the prior distribution in Bayesian inference and be able to articulate the usage of non-informative priors and conjugate priors;
- Show high level Interpretation of Bayesian Analysis Results and perform Bayesian model evaluation and assessment;
- Fit hierarchical models and provide thorough technical specifications for these models;
- Perform Bayesian computation using Markov chain Monte Carlo methods using R; and,
- Demonstrate how Bayesian Methods can be used to solve real world problems, including forming a hypothesis, collecting and analysing data, and reaching appropriate conclusions.
Throughout the course, relevant journal articles may be discussed as supplementary material. The final project will involve the application of methodology learned in the course to a real data set. Students will be required to formulate their own research questions, select and implement the appropriate statistical model(s), and write a report to communicate their findings.
”A First Course in Bayesian Statistical Methods”, Hoff, P. (2009). Springer: New York. (available online via the ANU library)
Technology, Software, Equipment:
You will be expected to perform data analyses using statistical software as part of your coursework. The official computer package for this course is R, which runs on Windows, MacOS and UNIX platforms. The software is free and available online through www.rproject.org: It is assumed students have a working knowledge of R from the pre-requisite course STAT2008/STAT2014. The use of other statistical programs is permitted but support will be provided solely for R.
1. ”Bayesian Data Analysis”. Gelman, A., Carlin, JB., Stern, HS., Dunson, DB., Vehtari, A., and Rubin, DB. (third edition) (2014). CRC Press: Florida. (available online via the ANU library)
2. "Statistical Rethinking: A Bayesian course with examples in R and Stan". McElreath, Richard. (second edition) (2020). Chapman and Hall. (available online via the ANU library)
Students will be given feedback in the following forms in this course:
- written comments
- verbal comments
- feedback to whole class, groups, individuals etc
ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.
As a further academic integrity control, students may be selected for a 15 minute individual oral examination of their written assessment submissions.
Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.”
|Week/Session||Summary of Activities||Assessment|
|1||Introduction to Bayesian inference; Review of probability (Hoff Chapters 1 and 2)|
|2||Bayesian inference for one parameter models (Hoff Chapter 3)|
|3||Bayesian inference for one parameter models (Hoff Chapter 3); Monte Carlo approximation and model checking (Hoff Chapter 4)||Tutorial Questions|
|4||Bayesian inference for the normal model (Hoff Chapter 5)|
|5||Gibbs sampling and MCMC convergence diagnostics (Hoff Chapter 6)||Online Test 1|
|6||Multivariate Normal Distribution (Hoff Chapter 7)||Tutorial Questions|
|7||Hierarchical Models (Hoff Chapter 8)||Tutorial Questions|
|8||Bayesian Linear Regression (Hoff Chapter 9 )||Tutorial Questions|
|9||Metropolis-Hastings Algorithm (Hoff Chapter 10)||Online Test 2|
|10||Mixed effects models (Hoff Chapter 11)||Tutorial Questions|
|11||Latent variable methods for ordinal data (Hoff Chapter 12); Bayesian models for missing data||Assignment 2 Due|
|12||Further topics in Bayesian Computation - computationally efficient MCMC (Variational Bayes, Hamilton Monte Carlo, Adaptive MCMC); Introduction to Bayesian Nonparametric models||Final Project Due in Exam Period|
Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. Students should enrol in their tutorial using MyTimetable. "ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage (https://www.anu.edu.au/students/program-administration/timetabling)".
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Tutorial Questions||5 %||08/08/2022||28/10/2022||LO1 - LO6|
|Online Test 1||15 %||25/08/2022||05/09/2022||LO1, LO2, LO3, LO4|
|Online Test 2||15 %||06/10/2022||17/10/2022||LO4, LO5, LO6, LO7|
|Assignment||25 %||21/10/2022||03/11/2022||LO4, LO5, LO6, LO7|
|Final Project||40 %||03/11/2022||01/12/2022||LO3 - LO7|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:
- Academic Integrity Policy and Procedure
- Student Assessment (Coursework) Policy and Procedure
- Special Assessment Consideration Guideline and General Information
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
- Code of practice for teaching and learning
The ANU is using 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. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.
Moderation of Assessment
Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.
Course content delivery will take the form of weekly on-campus lectures (recorded and available via echo360 on Wattle), weekly tutorials, delivered in hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos). Weekly consultations with the lecturer and the tutor(s) will be conducted over Zoom
Assessment Task 1
Learning Outcomes: LO1 - LO6
Before five of the weekly tutorial sessions, at the beginning of those weeks (see the class overview and Wattle for the exact date and time), you will submit your answers to tutorial questions online via Wattle. These will be graded for “performance” (whether you reasonably demonstrated the concepts) and not whether you got the answer correct. Each week the “performance” will be graded as 0 or 100. Feedback on tutorial submissions will given by the Friday of the same week that the submission is due.
Assessment Task 2
Learning Outcomes: LO1, LO2, LO3, LO4
Online Test 1
Online Test 1 will examine your understanding of basic Bayesian concepts. In particular, specification of a posterior distribution given a prior and likelihood function. Algebraic derivations, explanation of theoretical concepts, and interpretation of analytical results will be required. The online test may require you to run some R (or other statistical software package) code.
Online Test 1 is mandatory and will be open book. The duration of Online Test 1 is to be advised. Students will be required to download the test paper, and then scan and upload their answers to the online test on Wattle. Detailed instructions on how to complete the test online will be communicated in Week 4. Online Test 1 will be held on Thursday 25th August at a time to be advised. Online test 1 is to be done individually.
Due date: Thursday 25th August (Week 5)
Assessment Task 3
Learning Outcomes: LO4, LO5, LO6, LO7
Online Test 2
Online Test 2 will examine your understanding of the Gibbs sampling algorithm and its implementation in different Bayesian models. The online test may require you to run some R (or other statistical software package) code. Algebraic derivations, explanation of theoretical concepts, and interpretation of analytical results will be required.
Online Test 2 is mandatory and will be open book. The duration of Online Test 2 is to be advised. Students will be required to download the test paper, and then scan and upload their answers to the online test on Wattle. Detailed instructions on how to complete the test online will be communicated in Week 8. Online Test 2 will be held on Thursday 6th October at a time to be advised. Online test 2 is to be done individually.
Due date: Thursday 6th October (Week 9)
Assessment Task 4
Learning Outcomes: LO4, LO5, LO6, LO7
The assignment will require students to fit more complicated Bayesian models which may require implementation of the Gibbs sampling or Metropolis-Hastings algorithm. Algebraic derivations, exploration of theoretical topics and explanation of theoretical results and concepts may also be required. The assignment will be made available by the beginning of Week 8. The assignment is due on Friday 21st October.
The assignment is to be submitted electronically via Turnitin.
The assignment is mandatory and individual-based.
Due date: Friday 21st October 11:59pm (Week 11)
Assessment Task 5
Learning Outcomes: LO3 - LO7
The final project will involve application of material learned in the course to a real data set. Students may analyse a data set of their own choice (subject to lecturer approval)
or choose one of the data sets provided by the lecturer to analyse. Students will be required to formulate their own research question and demonstrate application of statistical methodology learned in STAT4116. Findings are to be communicated in a written report. The final project instructions will be made available by the end of Week 4. The Final Project is due on Thursday 3rd November. The final project is to be submitted electronically via Turnitin.
The final project is mandatory and individual-based.
Due date: Thursday 3rd November (first day of the Semester 2 final exam period)
Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.
The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.
The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.
The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.
You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education), submission of all assignments and reports must be submitted electronically through Turnitin.
All assignments are to be submitted electronically via Turnitin.
Late submissions without an approved extension from the course convenor are not permitted. Assessment tasks submitted after the due date without an approved extension will be awarded a mark of zero.
The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.
Resubmission of Assignments
Resubmission of assignments is not allowed after the due date.
Distribution of grades policy
Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.
Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.
Support for students
The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Access and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Bayesian analysis, missing data, data confidentiality
Dr Bronwyn Loong