Unit of study_

# STAT4028: Probability and Mathematical Statistics

## Overview

Probability Theory lays the theoretical foundations that underpin the models we use when analysing phenomena that involve chance. This unit introduces the students to modern probability theory and applies it to problems in mathematical statistics. You will be introduced to the fundamental concept of a measure as a generalisation of the notion of length and Lebesgue integration which is a generalisation of the Riemann integral. This theory provides a powerful unifying structure that bring together both the theory of discrete random variables and the theory of continuous random variables that were introduce to earlier in your studies. You will see how measure theory is used to put other important probabilistic ideas into a rigorous mathematical framework. These include various notions of convergence of random variables, 0-1 laws, and the characteristic function. You will then synthesise all these concepts to establish the Central Limit Theorem and also verify important results in Mathematical Statistics. These involve exponential families, efficient estimation, large-sample testing and Bayesian methods. Finally you will verify important convergence properties of the expectation-maximisation (EM) algorithm. By doing this unit you will become familiar with many of the theoretical building blocks that are required for any in-depth study in probability or mathematical statistics.

### Details

Academic unit Mathematics and Statistics Academic Operations STAT4028 Probability and Mathematical Statistics Semester 1, 2021 Normal day Camperdown/Darlington, Sydney 6

### Enrolment rules

 Prohibitions ? STAT4528 None None STAT3X23 or equivalent: that is, a sound working and theoretical knowledge of statistical inference. Yes

### Teaching staff and contact details

Coordinator Michael Stewart, michael.stewart@sydney.edu.au Michael Stewart Anna Natalia Aksamit

## Assessment

Type Description Weight Due Length
Final exam (Take-home short release) Final exam
Written examination
60% Formal exam period 2 hours
Outcomes assessed:
Assignment Probability Assignment
Written Assessment
20% Week 08 Half-semester
Outcomes assessed:
Assignment Mathematical Statistics Homework Week 7
Written Assessment
3% Week 08 Weekly
Outcomes assessed:
Assignment Mathematical Statistics Homework Week 8
Written Assessment
3% Week 09 Weekly
Outcomes assessed:
Assignment Mathematical Statistics Homework Week 9
Written Assessment
3.5% Week 10 Weekly
Outcomes assessed:
Assignment Mathematical Statistics Homework Week 10
Written Assessment
3.5% Week 11 Weekly
Outcomes assessed:
Assignment Mathematical Statistics Homework Week 11
Written Assessment
3.5% Week 12 Weekly
Outcomes assessed:
Assignment Mathematical Statistics Homework Week 12
Written Assessment
3.5% Week 13 Weekly
Outcomes assessed:
= Type D final exam
• Assignments/homework: These are writing tasks and will require you to integrate information from lectures and tutorials to create a concise written argument.
• Final exam: The exam will cover all material in the unit from both lectures and tutorials. The exam will have questions to answer. It is a 2h exam during the formal exam period.

Detailed information for each assessment would be given during the tutorial or can be found on Canvas.

### Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Representing complete or close to complete mastery of the material.

Distinction

75 - 84

Representing excellence, but substantially less than complete mastery.

Credit

65 - 74

Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.

Pass

50 - 64

Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

### Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

• Deduction of 5% of the maximum mark for each calendar day after the due date.
• After ten calendar days late, a mark of zero will be awarded.

### Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

The Current Student website provides information on academic honesty, academic dishonesty, and the resources available to all students.

The University expects students and staff to act ethically and honestly and will treat all allegations of academic dishonesty or plagiarism seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic dishonesty. If such matches indicate evidence of plagiarism or other forms of dishonesty, your teacher is required to report your work for further investigation.

## Weekly schedule

WK Topic Learning activity Learning outcomes
Week 01 Introduction to Measure Theory Lecture and tutorial (4 hr)
Week 02 Sigma Algebras and measurable functions Lecture and tutorial (4 hr)
Week 03 Lebesgue integrals Lecture and tutorial (4 hr)
Week 04 Convergence Lecture and tutorial (4 hr)
Week 05 Product Measures Lecture and tutorial (4 hr)
Week 06 Central Limit Theory Lecture and tutorial (4 hr)
Week 07 Exponential Families Lecture and tutorial (4 hr)
Week 08 Regular Parametric Models Lecture and tutorial (4 hr)
Week 09 Efficient Estimation Lecture and tutorial (4 hr)
Week 10 Efficient Testing Lecture and tutorial (4 hr)
Week 11 Optimality of Bayesian Methods Lecture and tutorial (4 hr)
Week 12 EM Algorithm Lecture and tutorial (4 hr)
Week 13 Revision week Lecture and tutorial (4 hr)

### Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

There are no specific prescribed readings for this unit.

## Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University’s graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

• LO1. Demonstrate a coherent and advanced knowledge of the concepts of measure theory and Lebesgue integration and how they provide a unified approach to a wide variety of problems arising in probability.
• LO2. Communicate mathematical analyses and solutions to mathematical and practical problems in probability and related fields clearly in a variety of media to diverse audiences.
• LO3. Utilise characteristic function techniques to prove foundational theoretical results in probability.
• LO4. Compare and contrast different forms of stochastic convergence.
• LO5. Construct optimal solutions to a wide variety of problems in mathematical statistics.
• LO6. Devise solutions to novel mathematical problems in probability theory.

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

 GQ1 Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline. GQ2 Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem. GQ3 Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context. GQ4 Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies. GQ5 Inventiveness Generating novel ideas and solutions. GQ6 Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues. GQ7 Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries. GQ8 Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context. GQ9 Influence Engaging others in a process, idea or vision.