Stochastic Simulation

6 EC

Semester 1, period 1, 2

5334STSI6Y

Owner Master Mathematics
Coordinator prof. dr. M.R.H. Mandjes
Part of Master Mathematics, year 1Master Stochastics and Financial Mathematics, year 1

Course manual 2017/2018

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 the course website: https://staff.fnwi.uva.nl/j.l.dorsman/stochSim1718.html

Objectives

 The learning objectives for this course are as follows. After this course, the student

  • understands the abovementioned simulation techniques and is able toapply them to a wide variety of stochastic processes;
  • is able to build a correct simulation model from a business context,
  • 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

The programme does not have requirements concerning attendance (OER-B).


Additional requirements for this course:

Additional requirements for this course:

None.

Assessment

Item and weight Details

Final grade

0.6 (60%)

Written or Exam, depending on student numbers

Must be ≥ 5

0.4 (40%)

Homework

Assignments

Homework

  • During the course, four homework sets will be given, comprised of both theoretical and practical exercises. Each of these homework sets will be assessed and will count 10% towards the 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.uva.nl/plagiarism

Course structure

The structure of the course will be communicated through the course website: https://staff.fnwi.uva.nl/j.l.dorsman/stochSim1718.html

Timetable

The schedule for this course is published on DataNose.

Contact information

Coordinator

  • prof. dr. M.R.H. Mandjes

Co-lecturer: Dr. J.L Dorsman