Cricos Provider Code: 00098G




UNSW SCIENCE
School of Maths and Statistics

Course outline

MATH3871 / MATH5960
Bayesian Inference and Computation

Term 3, 2021


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Staff

Position Name Email Room
Lecturer-in-charge Dr Clara Grazian c.grazian@unsw.edu.au RC-2056

Please refer to your Timetable on MyUNSW for your Lecture Tut, Lab enrolment days and times.
MATH3871 Timetable weblink: http://timetable.unsw.edu.au/2021/MATH3871.html
MATH5960 Timetable weblink: http://timetable.unsw.edu.au/2021/MATH5960.html

Administrative Contacts

Please visit the School of Mathematics and Statistics website for a range of information on School
Policies, Forms and Help for Students.

For information on Courses, please go to “Current Students” and either Undergraduate and/or
Postgraduate”, Course Homepage” for information on all course offerings,

The “Student Notice Board” can be located by going to the “Current Students” page; Notices are
posted regularly for your information here. Please familiarise yourself with the information found in
these locations. The School web page is: http://www.maths.unsw.edu.au

If you cannot find the answer to your queries on the web you are welcome to contact the Student
Services Office directly.

By email Undergraduate ug.mathsstats@unsw.edu.au
Postgraduate pg.mathsstats@unsw.edu.au
By phone: 9385 7053

Should we need to contact you, we will use your official UNSW email address of in the first
instance. It is your responsibility to regularly check your university email account. Please
state your student number in all emails.

Course Aims

? Provide a strong background in the concepts and philosophy of Bayesian inference.
? Instil an appreciation of the benefits of the Bayesian framework.
? Provide extensive practical opportunities to implement Bayesian data analyses.
? Present an overview of research activity in this field.
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Course Description

After describing the fundamentals of Bayesian inference, this course will examine the specification
of prior and posterior distributions, Bayesian decision theoretic concepts, the ideas behind
Bayesian hypothesis tests, model choice and model averaging, and evaluate the capabilities of
several common model types, such as hierarchical and mixture models. An important part of
Bayesian inference is the requirement to numerically evaluate complex integrals on a routine
basis. Accordingly, this course will also introduce the ideas behind Monte Carlo integration,
importance sampling, rejection sampling, Markov chain Monte Carlo samplers such as the Gibbs
sampler and the Metropolis-Hastings algorithm and use of the WinBuGS posterior simulation
software.

Assessment and Deadlines

Assessment Weighting % Due date if applicable
Assignment 1: Quiz 15% Week 3, 1.10.21 at 5pm
(Provided one week prior)
Assignment 2: Quiz
(Including open questions)
20% Week 7, 29.10.21 at 5pm
(Provided one week prior)
Assignment 3: Short quizzes 5% Weeks 2, 4, 5, 8 & 9
(Open at the beginning of lectures
in these weeks)
Final Exam 60%


Late Submission of Assessment Tasks
A late penalty of 5% of the awarded mark will be applied per day or part day any assessment task
is submitted more than 1 hour late. (Where "late" in this context means after any extensions
granted for Special Consideration or Equitable Learning Provisions.) For example, an assessment
task that was awarded 75% would be given 65% if it was 1-2 days late. Any assessment task
submitted 7 or more days late will be given zero.

Note that the penalty does not apply to
? Assessment tasks worth less than 5% of the total course mark, e.g. weekly quizzes,
weekly class participation, or weekly homework tasks.
? Examinations and examination-style class tests
? Pass/Fail Assessments


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Course Schedule

The course will include material taken from some of the following topics. This is should only serve
as a guide as it is not an extensive list of the material to be covered and the timings are
approximate. The course content is ultimately defined by the material covered in lectures.

Weeks Topic Reading (if
applicable)
1 Introduction to subjective probability and differences
between Bayesian and classical statistics
Refer to Moodle

2 Prior and posterior distributions Refer to Moodle

3 Point estimation, interval estimation and predictive
distributions
Refer to Moodle

4 Bayesian analysis of normal models Refer to Moodle

5 Introduction to Monte Carlo methods Refer to Moodle

7 MCMC methods Refer to Moodle

8 Bayesian hypothesis testing Refer to Moodle

9 Linear and generalised linear models Refer to Moodle

10 Hierarchical models Refer to Moodle


Textbooks

Suggested books, you can choose one of the two, none of them is required.
? Hoff, P. D. (2009). A first course in Bayesian statistical methods (Vol. 580). New York:
Springer.
? Reich, B. J., & Ghosh, S. K. (2019). Bayesian statistical methods. CRC Press.

Course Learning Outcomes (CLO)

? Provide a background in the concepts and philosophy of Bayesian inference.
? Demonstrate an understanding of how common model type’s work and be able to construct
models for new problems.
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? Show an appreciation of the importance of computational techniques in Bayesian
inference.
? Perform real-world Bayesian data analyses.

Moodle

Log in to Moodle to find announcements, general information, notes, lecture slide, classroom tutorial
and assessments etc.
http://moodle.telt.unsw.edu.au

School and UNSW Policies

The School of Mathematics and Statistics has adopted a number of policies relating to enrolment,
attendance, assessment, plagiarism, cheating, special consideration etc. These are in addition to
the Policies of The University of New South Wales. Individual courses may also adopt other
policies in addition to or replacing some of the School ones. These will be clearly notified in the
Course Initial Handout and on the Course Home Pages on the Maths Stats web site.

Students in courses run by the School of Mathematics and Statistics should be aware of the School
and Course policies by reading the appropriate pages on the Maths Stats web site starting at:
http://www.maths.unsw.edu.au/currentstudents/assessment-policies

The School of Mathematics and Statistics will assume that all its students have read and
understood the School policies on the above pages and any individual course policies on the
Course Initial Handout and Course Home Page. Lack of knowledge about a policy will not be an
excuse for failing to follow the procedure in it.

Academic Integrity and Plagiarism

UNSW has an ongoing commitment to fostering a culture of learning informed by academic
integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic
integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at
UNSW is defined as using the words or ideas of others and passing them off as your own.
The UNSW Student Code provides a framework for the standard of conduct expected of UNSW
students with respect to their academic integrity and behaviour. It outlines the primary obligations
of students and directs staff and students to the Code and related procedures.
In addition, it is important that students understand that it is not permissible to buy essay/writing
services from third parties as the use of such services constitutes plagiarism because it involves
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using the words or ideas of others and passing them off as your own. Nor is it permissible to sell
copies of lecture or tutorial notes as students do not own the rights to this intellectual property.
If a student breaches the Student Code with respect to academic integrity, the University may take
disciplinary action under the Student Misconduct Procedure.
The UNSW Student Code and the Student Misconduct Procedure can be found at:
http://student.unsw.edu.au/plagiarism
An online Module “Working with Academic Integrity” (http://student.unsw.edu.au/aim) is a six-
lesson interactive self-paced Moodle module exploring and explaining all of these terms and
placing them into your learning context. It will be the best one-hour investment you’ve ever made.
Plagiarism

Plagiarism is presenting another person's work or ideas as your own. Plagiarism is a serious
breach of ethics at UNSW and is not taken lightly. So how do you avoid it? A one-minute video for
an overview of how you can avoid plagiarism can be found http://student.unsw.edu.au/plagiarism.

Additional Support
ELISE (Enabling Library and Information Skills for Everyone)

ELISE is designed to introduce new students to studying at UNSW.
Completing the ELISE tutorial and quiz will enable you to:
? analyse topics, plan responses and organise research for academic writing and other
assessment tasks
? effectively and efficiently find appropriate information sources and evaluate relevance to
your needs
? use and manage information effectively to accomplish a specific purpose
? better manage your time
? understand your rights and responsibilities as a student at UNSW
? be aware of plagiarism, copyright, UNSW Student Code of Conduct and Acceptable Use of
UNSW ICT Resources Policy
? be aware of the standards of behaviour expected of everyone in the UNSW community
? locate services and information about UNSW and UNSW Library
Some of these areas will be familiar to you, others will be new. Gaining a solid understanding of all
the related aspects of ELISE will help you make the most of your studies at UNSW.

The ELISE training webpages:
http://subjectguides.library.unsw.edu.au/elise/aboutelise

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Equitable Learning Services (ELS)

If you suffer from a chronic or ongoing illness that has, or is likely to, put you at a serious
disadvantage, then you should contact the Equitable Learning Services (previously known as
SEADU) who provide confidential support and advice.
They assist students:
? living with disabilities
? with long- or short-term health concerns and/or mental health issues
? who are primary carers
? from low SES backgrounds
? of diverse genders, sexes and sexualities
? from refugee and refugee-like backgrounds
? from rural and remote backgrounds
? who are the first in their family to undertake a bachelor-level degree.
Their web site is: http://student.unsw.edu.au/els/services

Equitable Learning Services (ELS) may determine that your condition requires special
arrangements for assessment tasks. Once the School has been notified of these, we will make
every effort to meet the arrangements specified by ELS.

Additionally, if you have suffered significant misadventure that affects your ability to complete the
course, please contact your Lecturer-in-charge in the first instance.

Academic Skills Support and the Learning Centre

The Learning Centre offers academic support programs to all students at UNSW Australia. We
assist students to develop approaches to learning that will enable them to succeed in their
academic study. For further information on these programs please go to:
http://www.lc.unsw.edu.au/services-programs

Applications for Special Consideration for Missed Assessment

Please adhere to the Special Consideration Policy and Procedures provided on the web page
below when applying for special consideration.
http://student.unsw.edu.au/special-consideration

Please note that the application is not considered by the Course Authority, it is considered by a
centralised team of staff at the Nucleus Student Hub.
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The School will contact you (via student email account) after special consideration has been
granted to reschedule your missed assessment, for a lab test or paper-based test only.

For applications for special consideration for assignment extensions, please note that the new
submission date and/or outcome will be communicated through the special consideration web site
only, no communication will be received from the School.

For Dates on Final Term Exams and Supplementary Exams please check the “Key Dates for
Exams” ahead of time to avoid booking holidays or work obligations.
http://student.unsw.edu.au/exam-dates

If you believe your application for Special Consideration has not been processed, you should email
specialconsideration@unsw.edu.au immediately for advice.

Course Evaluation and Development (MyExperience)

Student feedback is very important to continual course improvement. This is demonstrated within
the School of Mathematics and Statistics by the implementation of the UNSW online student
survey myExperience, which allows students to evaluate their learning experiences in an
anonymous way. myExperience survey reports are produced for each survey. They are released
to staff after all student assessment results are finalised and released to students. Course
convenor will use the feedback to make ongoing improvements to the course.