MATH5835 Advanced Stochastic Processes - 2024
General Course Information
Course Code : MATH5835
Year : 2024
Term : Term 1
Teaching Period : T1
Course Details & Outcomes
Course Description
This is a Postgraduate level course in Stochastic Processes for students in Mathematics and Statistics. The theory of stochastic processes deals with phenomena evolving randomly in time and/or space, such as prices on fnancial markets, air temperature or wind velocity, spread of diseases, number of hospital admissions in certain area, and many others. This course introduces some of the basic ideas and tools to study such phenomena. In particular, we will introduce the concept of martingale to study phenomena evolving in discrete time and the concept of Poisson process (and its generalizations) and Brownian Motion to study processes evolving continuously in time.
Some applications to statistical inference will also be discussed. The course will also equip you with the foundational knowledge for more advanced courses in the Master of Financial Mathematics program. There will be four hours of lectures and one hour of tutorial/consultation each week.
Pre-requisites: 24 units of level III mathematics (including 6 units in MATH3801 Probability and Stochastic Processes or MATH3901 Higher Probability and Stochastic Processes or an equivalent course) or a degree in a numerate discipline or permission of the Head of Department.
Course Aims
The aim of this course is to introduce some of the foundational ideas and tools of the theory of stochastic processes. The course features the fundamental concepts of measure and probability, integral and expectation, conditional expectation, independence, special dependence structures such as martingales and Markov processes, and some basic continuous processes such as Poisson processes and Brownian motion. In addition to familiarise students with the basic types of stochastic processes often encountered in application, the course also intends to equip the students with a level of mathematical maturity needed for the more advanced courses in the Master of Financial Mathematics.
Course Learning Outcomes
Course Learning Outcomes |
CLO1 : State the most rudimentary concepts and theorems of measure-theoretic probability theory. |
CLO2 : Defne the properties of various stochastic process models and related random elements. |
CLO3 : Identify appropriate stochastic process model(s) for a given problem. |
CLO4 : Provide logical and coherent proofs of important theoretic results. |
CLO5 : Apply the theory of stochastic processes to model real phenomena and answer some questions in applied sciences. |
Course Learning Outcomes |
Assessment Item |
CLO1 : State the most rudimentary concepts and theorems of measure-theoretic probability theory. |
• Assignment 1 • Assignment 2 • Assignment 3 • Final Examination |
CLO2 : Defne the properties of various stochastic process models and related random elements. |
• Assignment 1 • Assignment 2 • Assignment 3 • Final Examination |
CLO3 : Identify appropriate stochastic process model(s) for a given problem. |
• Assignment 1 • Assignment 2 • Assignment 3 • Final Examination |
CLO4 : Provide logical and coherent proofs of important theoretic results. |
• Assignment 1 • Assignment 2 • Assignment 3 • Final Examination |
CLO5 : Apply the theory of stochastic processes to model real phenomena and answer some questions in applied sciences. |
• Assignment 3 • Final Examination |
Learning and Teaching Technologies
Moodle - Learning Management System | Zoom | Echo 360
Assessments
Assessment Structure
Assessment Item |
Weight |
Relevant Dates |
Assignment 1 Assessment Format: Group |
15% |
Start Date: Not Applicable Due Date: Week 4: 04 March - 10 March |
Assignment 2 Assessment Format: Group |
15% |
Due Date: Week 8: 01 April - 07 April |
Assignment 3 Assessment Format: Individual |
10% |
Due Date: Week 10: 15 April - 21 April |
Final Examination Assessment Format: Individual |
60% |
Start Date: In the exam period Due Date: Two hours after opening the exam booklet |
Assessment Details
Assignment 1
Assessment Overview
Working in groups, you will solve proof or calculation questions related to basic concepts in
probability theory and stochastic processes. You will have the chance to demonstrate your
understanding and mastery of the fundamental concepts and theory. You have three weeks to complete this assignment, with the submission due in Week 4. Feedback will be provided within 2 weeks of submission.
Course Learning Outcomes
CLO1 : State the most rudimentary concepts and theorems of measure-theoretic probability theory.
CLO2 : Defne the properties of various stochastic process models and related random elements.
CLO3 : Identify appropriate stochastic process model(s) for a given problem. CLO4 : Provide logical and coherent proofs of important theoretic results.
Assessment Length
10 pages maximum
Submission notes
Typed up or neatly handwritten
Assignment submission Turnitin type
Not Applicable
Assignment 2
Assessment Overview
Working in groups, you will solve proof or calculation questions related to stochastic processes and their applications. In the process, you shall have the chance to demonstrate your
understanding and mastery of the theory and applications of relevant stochastic processes. You have three weeks to complete this assignment, with the submission due in Week 8. Feedback
will be provided within two weeks of submission.
Course Learning Outcomes
· CLO1 : State the most rudimentary concepts and theorems of measure-theoretic probability theory.
CLO2 : Defne the properties of various stochastic process models and related random elements.
CLO3 : Identify appropriate stochastic process model(s) for a given problem. · CLO4 : Provide logical and coherent proofs of important theoretic results.
Assessment Length
10 pages maximum
Submission notes
Typed up or neatly handwritten
Assignment submission Turnitin type
Not Applicable
Assignment 3
Assessment Overview
Working individually, you will solve practical or theoretical questions related to stochastic
processes and their applications. In the process, you shall have the chance to demonstrate your understanding and mastery of the theory and applications of relevant stochastic processes. You have three weeks to complete this assignment, with the submission due in Week 10. Feedback will be provided within two weeks of submission.
Course Learning Outcomes
· CLO1 : State the most rudimentary concepts and theorems of measure-theoretic probability theory.
· CLO2 : Defne the properties of various stochastic process models and related random elements.
· CLO3 : Identify appropriate stochastic process model(s) for a given problem. · CLO4 : Provide logical and coherent proofs of important theoretic results.
· CLO5 : Apply the theory of stochastic processes to model real phenomena and answer some questions in applied sciences.
Assessment Length
6 pages maximum
Submission notes
Typed up or neatly handwritten
Assignment submission Turnitin type
Not Applicable
Final Examination
Assessment Overview
In the two hour exam, you will answer 4-5 questions related to the materials covered in this course. The difcultylevel of the questions will be comparable to those in your assignments and the sample example paper. The exam is held during the formal examination period, with feedback available through inquiry with the convenor.
Course Learning Outcomes
CLO1 : State the most rudimentary concepts and theorems of measure-theoretic probability theory.
CLO2 : Defne the properties of various stochastic process models and related random elements.
CLO3 : Identify appropriate stochastic process model(s) for a given problem. CLO4 : Provide logical and coherent proofs of important theoretic results.
CLO5 : Apply the theory of stochastic processes to model real phenomena and answer some questions in applied sciences.
Assignment submission Turnitin type
Not Applicable
General Assessment Information
With the group assignments (Assignments 1 and 2), you must work in groups with around 5 members. You should collaborate in working out the answers to the assignment questions and make a single group submission by the due date. All members of a group should ensure you understand and approve of what is included in your group submission, and all members must sign their names on the cover of the submission, although only one member needs to click the 'submit'button (after all members agree to the submission). At the time of submission, all members of a group are required to do a short survey to evaluate and score group members' performance and contributions to the group work, and each member's assignment mark will be infuenced by the results of this peer evaluation process, so members in the same group might have different marks in the end.
With the individual assignment (Assignment 3), you can choose to work individually or in a group
(up to 5 members). If you choose to work in a group, you accept that your mark for this
assignment will be the same as your group members' (there will be no peer evaluation for this assignment).
Finally, please note that for assignments that are submitted after the due time, the standard late submission penalties apply.
Grading Basis
Standard
Requirements to pass course
Achieving a composite score of 50 out 100 at least
Course Schedule
Teaching Week/Module |
Activity Type |
Content |
Week 0 : 5 February - 11 February |
Other |
Orientation week, no formal classes. |
Week 1 : 12 February - 18 February |
Lecture |
Introduction to measure-theoretic probability |
Week 2 : 19 February - 25 February |
Lecture |
General notations of stochastic processes |
Week 3 : 26 February - 3 March |
Lecture |
Martingales |
Week 4 : 4 March - 10 March |
Lecture |
Martingales (continued) |
Week 5 : 11 March - 17 March |
Lecture |
Markov processes |
Week 6 : 18 March - 24 March |
Reading |
Flexibility week (no classes) |
Week 7 : 25 March - 31 March |
Lecture |
Markov processes (continued) |
Week 8 : 1 April - 7 April |
Lecture |
Poisson processes |
Week 9 : 8 April - 14 April |
Lecture |
Brownian motion |
Week 10 : 15 April - 21 April |
Lecture |
Ito integral |
Attendance Requirements
Students are strongly encouraged to attend all classes and review lecture recordings.
General Schedule Information
The following is a rough schedule we try to follow during the course.
Course Resources
Prescribed Resources
Lecture notes and other materials posted on Moodle
Recommended Resources
Foundations of Modern Probability, by Olav Kallenberg (any edition)
A Course in Probability, by Kai Lai Chung (third edition)
Stochastic Processes: From Applications to Theory, by Pierre Del Moral and Spiridon Penev
Course Evaluation and Development
Students suggested in feedback to include more examples and exercises, and we have tutorial classes to discuss examples and exercises and to address any questions student might have about the course materials.
Staff Details
Position |
Name |
|
Location |
Phone |
Availability |
Equitable Learning Services Contact |
Primary Contact |
Lecturer |
Feng Chen |
|
Anita B. Lawrence Centre 1031 |
|
Appointment via email. |
No |
Yes |