STATS 225 : Probability: Theory and Applications
Science
2020 Semester One (1203) (15 POINTS)
Course Prescription
Course Overview
This course will provide an introduction to probability through some classical theory, useful techniques, and stochastic models fundamental to many applications. It should provide a solid mathematical foundation for more advanced courses in probability or mathematical statistics, and is important for anyone who wants to advance to honours or masters level in these areas.
Topics may include: Probability measures. Event spaces. Borel sigma-algebra. Properties of probabilities, including continuity of probability. Fundamental model of Uniform probability measure on [0,1] and Lebesgue-Borel Theorem (statement). Independence. Borel-Cantelli Lemmas. Random variables. Expectation. Monotone Convergence Theorem (statement). Conditional probability and expectation. Conditional expectation with respect to a random variable. Generating functions techniques: PGFs, MGFs, Characteristic functions and Laplace transforms. Convergence of generating functions. Types of convergence. Weak Law of Large Numbers. Strong Law of Large Numbers (proof of special case). Central Limit Theorem (sketch proof). Random walks. First return times. First passage times. Gambler's ruin. Reflection principle. Ballot Theorem. Recurrence of random walks. Branching processes: discrete time Galton-Watson process, extinction probabilities, population size. Poisson processes: characterisations, inter-arrival times, gamma distributions, thinning and conditional uniformity. Poisson point processes (PPPs) on R^n. Examples of PPPs with non-constant intensities.
Course Requirements
Capabilities Developed in this Course
| Capability 1: | Disciplinary Knowledge and Practice |
| Capability 2: | Critical Thinking |
| Capability 3: | Solution Seeking |
| Capability 4: | Communication and Engagement |
| Capability 5: | Independence and Integrity |
Learning Outcomes
- Demonstrate an understanding of the main concepts underlying probability theory (Capability 1, 2 and 4)
- Work effectively with probabilities, expectations, and random variables (Capability 1, 2 and 3)
- Apply generating function methods to solve various problems in probability (Capability 1, 2 and 3)
- Organise a mathematical calculation for coherence and readability (Capability 1, 2, 3, 4 and 5)
- Produce clearly reasoned proofs in probability at an appropriate level of difficulty (Capability 1, 2, 3, 4 and 5)
- Understand and use fundamental stochastic models and their basic properties to solve problems in probability (Capability 1, 2, 3, 4 and 5)
- Solve a variety of problem in probability (Capability 1, 2, 3 and 5)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Coursework | 30% | Individual Coursework |
| Final Exam | 60% | Individual Examination |
| Test | 10% | Individual Test |
| 3 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | ||||
| Coursework | ||||||||||
| Final Exam | ||||||||||
| Test | ||||||||||
A mark of at least 40% in the final exam and 50% overall is required to pass this course.
Learning Resources
"Probability: an introduction" by G.R.Grimmett and D. Welsh (Oxford University Press)
Workload Expectations
This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.
For this course, each week you can typically expect 3 hours of lectures, a 1 hour tutorial, 2-3 hours of reading and thinking about the content, and 3-4 hours of work on assignments and/or test preparation.
Digital Resources
Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).
Please remember that the recording of any class on a personal device requires the permission of the instructor.
Copyright
The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.
You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.
Academic Integrity
The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.
Inclusive Learning
All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.
Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website at http://disability.auckland.ac.nz
Special Circumstances
If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.
If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page: https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.
This should be done as soon as possible and no later than seven days after the affected test or exam date.
Student Feedback
During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.
At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.
Your feedback helps to improve the course and its delivery for all students.
Student Charter and Responsibilities
The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter (https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html).
Disclaimer
Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.