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TOKT1160 — STOCHASTIC PROCESSES | Chương trình đào tạo NEU

TOKT1160 — STOCHASTIC PROCESSES

Credits
3
Self-study hours
90
Showing intake
K68-2026

Course description

This course introduces fundamental concepts and methods of stochastic processes with applications in economics, finance, and actuarial science. Students will learn how to model randomness evolving over time, focusing on discrete and continuous time processes. Topics include Markov chains, branching processes, Poisson processes, continuous-time Markov processes, martingales, Brownian motion, and stochastic differential equations. Emphasis is placed on both theoretical foundations and applications, equipping students with tools for probabilistic modeling, risk management, and decision-making in uncertain environments.

Course requirements

Requirements for Final Examination

Attendance score must be at least 5 (according to University regulations).

See Appendix 2, Rubric 1 for attendance evaluation.

Requirements for Class Attendance

Students are responsible for attending all scheduled classes.

In case of absence due to force majeure, students must actively acquire the knowledge through the materials provided by the lecturer and other assigned readings.

Requirements for In-class Behaviour

The course is conducted on the principle of mutual respect between lecturers and students.

Students are required to take full notes and participate in answering questions.

Students must read assigned materials in advance.

Homework assignments must be completed as required.

Students must not make noise and disturb others.

Laptops, tablets, and mobile phones may only be used for note-taking, calculations, or learning purposes, and must not be used for any other activities.

Lecturers

  • Vuong Van Yen
  • Nguyễn Quang Huy

Teaching plan

Week 1

Introduction + Intro to stochastic processes + Deterministic and probabilistic models + Probability review: Conditional Expectation, Conditional Independence
References
- Syllabus - [1] Chapter 1
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check

Week 2

Markov Chains I + Definition, examples + Basic computations + Chapman–Kolmogorov equation
References
[1] Chapter 2
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 3

Markov Chains II + Limiting distribution + Stationary distribution
References
[1] Chapter 2, 3
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 4

Markov Chains III + Classification of states + Irreducible Markov Chains + First step analysis
References
[1] Chapter 3
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 5

Markov Chains IV + Ergodic Theorem + Absorbing chains + Strong Markov property (optional)
References
[1] Chapter 3
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 6

Markov Chain Monte Carlo + Metropolis-Hastings Algorithm + Gibbs Sampling
References
[1] Chapter 5
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 7

Branching Processes + Mean generation size + Extinction Probability
References
[1] Chapter 4
Assessment
Criteria: Calculation problems Tools: Rubric 1, 2 Method: Written Exam

Week 8

Poisson Process I + Definition, basic computations + Arrival, interarrival times + Infinitesimal probabilities + Thinning, Superposition
References
[1] Chapter 6
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 9

Poisson Process II + Conditional arrival times as uniform distributions + Compound Poisson process + Nonhomogeneous Poisson process
References
[1] Chapter 6
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 10

Continuous Time Markov Process + Transition rates + Infinitesimal generator + Long-term behavior
References
[1] Chapter 7
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 11

Martingales + Definition, examples + Optional stopping theorem + Applications in finance
References
[1] Chapter 8
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 12

Brownian Motion + Definition + Gaussian process + Transformations and Properties + Variations and Applications
References
[1] Chapter 8
Assessment
Criteria: Calculation problems Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 13

Introduction to Stochastic Calculus + Stochastic Integration, Differentiation + Ito’s lemma
References
[1] Chapter 9
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Week 14

Stochastic Differential Equations + Recall about ODEs + Diffusions + Solving SDEs + Examples
References
[1] Chapter 9
Assessment
Criteria: Calculation problems Tools: Rubric 1, 2 Method: Written Exam

Week 15

Summary
Assessment
Criteria: Attendance, Home exercise and Discussion Tools: Rubric 1 Method: Attendance Check & Work Assignment

Learning resources

  • 1. Course Books
  • R. Dobrow. Introduction to Stochastic Processes with R. Wiley, 2016
  • 2. References
  • R. Durrett. Essentials of Stochastic Processes. Springer, 2016