Stochastic Simulation

6 EC

Semester 1, period 1, 2

5334STSI6Y

Owner Master Mathematics
Coordinator dr. J.L. Dorsman
Part of Master Mathematics, specialization Mathematical Physics, year 1Master Mathematics, specialization Stochastics , year 1Master Stochastics and Financial Mathematics, specialization Data Analysis and Statistics, year 1Master Stochastics and Financial Mathematics, specialisation Probability and Decision Making, year 1Master Mathematics, specialization Algebra and Geometry, year 1Master Mathematics, specialization Analysis and Dynamical Systems, year 1Master Mathematics, specialisation Discrete Mathematics and Quantum Information, year 1Master Stochastics and Financial Mathematics, specialisation Probability and Decision Making, year 1Master Stochastics and Financial Mathematics, specialization Data Analysis and Statistics, year 1Master Stochastics and Financial Mathematics, specialization Financial Mathematics, year 1

Course manual 2023/2024

Course content


The field of advanced simulation contains powerful tools and techniques to study stochastic processes and other objects which defy a direct mathematical analysis. This course gives a broad treatment of the important aspects of stochastic simulation and its applications to e.g. queueing, reliability, manufacturing, risk analysis, and financial models. Aside from the fundamental mathematical interests, this course is thus also recommended for students wishing to make a career in business, finance, operations research, etc. In particular, we will discuss a selection of the following simulation concepts and techniques:

1. Random number generation

2. Discrete event simulation

3. Output analysis of simulation results

4. Steady-state simulation

5. Variance-reduction methods

6. Rare-event simulation

7. Derivative estimation

8. Simulation of complex stochastic processes, such as Gaussian processes or time series.

9. Simulated annealing

Study materials

Literature

  • Soren Asmussen, Peter W. Glynn
    Stochastic Simulation: Algorithms and Analysis
    Springer Verlag 2007
    Volume 57 of the series Stochastic Modelling and Applied Probability
    ISBN: 9780387306797

Other

  • Additional handouts will be made available through Canvas.

Objectives

  • The student understands the above mentioned simulation techniques and is able to apply them to a wide variety of stochastic processes;
  • is able to build a correct simulation model from a business context, and
  • is able to perform an efficient simulation based on this model to capture the dynamics of key performance measures, after which the student
  • is able to successfully analyse and interpret the simulation results using appropriate statistical methods.

Teaching methods

  • Self-study
  • Lecture

Learning activities

Activity

Number of hours

Classes

30

Self-study and assignments

138

Attendance

This programme does not have requirements concerning attendance (TER-B).

Additional requirements for this course:

None.

Assessment

Item and weight Details

Final grade

60%

Tentamen

Must be ≥ 5, final grade

40%

Homework

The exam counts for 60% of the final grade, while there will be homework which in total account for 40% of the final grade.

The exam is either an oral or a written exam, depending on student numbers. The grade of the exam must be at least a 5.0. If this is not the case, the final grade of the course will be the exam grade, which implies a failure of the course.

When the final grade is insufficient, there is the possibility of taking the resit exam. This retake counts for 100%, so previously made homework will not count towards the resit final grade.

Inspection of assessed work

Contact the course coordinator to make an appointment for inspection.

Assignments

Homework

  • During the course, several homework sets will be given, comprised of both theoretical and practical exercises. The average of the grades acquired with the homework sets will be the homework grade that counts 40% towards your final grade.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

The structure of the course will be communicated through canvas.

Timetable

The schedule for this course is published on DataNose.

Contact information

Coordinator

  • dr. J.L. Dorsman

Staff

  • J.M. Mooij
  • T.G.C. Nijsten