SDST3603 Stochastic processes (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota ---
Course Co-ordinator Prof C Wang, SCDS (Department of Statistics and Actuarial Science) < stacw@hku.hk >
Teachers Involved (Prof C Wang,Statistics & Actuarial Science)
Course Objectives This is an introductory course in stochastic processes.  It will cover the basic concepts of the theory of stochastic processes and explore different types of stochastic processes including Markov chains, Poisson processes and Brownian motions.
Course Contents & Topics Introduction to probability theory, conditional probability and expectation, Markov chains, random walk models, classification of states in a Markov chain, calculation of limiting probabilities and mean time spent in transient states, Poisson process, distribution of inter-arrival time and waiting time, conditional distribution of the arrival time, Brownian Motion, hitting time and maximum variable, geometric Brownian motion, the Black-Scholes option pricing formula.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 apply the conditioning method to calculate the mean and probability
CLO 2 understand the essentials of Markov chains, the Poisson process, and Brownian motion
CLO 3 understand how stochastic models can be applied to the study of real-life phenomena
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in SDST2601; and
Not for students who have passed in MATH3603, or have already enrolled in this course; and
Not for students who have passed in SDST3903, or have already enrolled in this course.
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor /Professional Core 2U000C00 Course not offered under any Major/Minor/Professional core
2024 Major in Risk Management ( Disciplinary Elective )
2024 Major in Statistics ( Disciplinary Elective )
2024 Minor in Statistics ( Disciplinary Elective )
2023 Major in Risk Management ( Disciplinary Elective )
2023 Major in Statistics ( Disciplinary Elective )
2023 Minor in Statistics ( Disciplinary Elective )
2022 Major in Risk Management ( Disciplinary Elective )
2022 Major in Statistics ( Disciplinary Elective )
2022 Minor in Statistics ( Disciplinary Elective )
2021 Major in Risk Management ( Disciplinary Elective )
2021 Major in Statistics ( Disciplinary Elective )
2021 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping 2024 Major in Risk Management < PLO 2,3,4 >
2024 Major in Statistics < PLO 1,2,4,5,6 >
2023 Major in Risk Management < PLO 2,3,4 >
2023 Major in Statistics < PLO 1,2,4,5,6 >
2022 Major in Risk Management < PLO 2,3,4 >
2022 Major in Statistics < PLO 1,2,4,5,6 >
2021 Major in Risk Management < PLO 2,3,4 >
2021 Major in Statistics < PLO 1,2,4,5,6 >
Offer in 2025 - 2026 Y        1st sem    Examination Dec     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Lectures 36.0
Tutorials 12.0
Reading / Self study 100.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments Coursework (assignments, tutorials, and a class test) 40.0 1,2,3
Examination One 2-hour written examination 60.0 1,2,3
Required/recommended reading
and online materials		 S. M. Ross: Introduction to Probability Models (9th edition)
Course Website http://moodle.hku.hk
Additional Course Information NIL