5334STSI6Y - Stochastic Simulation (Fall 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:
  • Random number generation
  • Discrete event simulation
  • Output analysis of simulation results
  • Steady-state simulation
  • Variance-reduction methods
  • Rare-event simulation
  • Derivative estimation
  • Simulation of complex stochastic processes, such as Gaussian processes or time series.
  • Simulated annealing
Many of these topics require a thorough understanding of basic probability theory and statistics.

Learning objectives

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

Lecturers

  • Prof. dr. M.R.H. Mandjes, tel.: 020-5255164, e-mail: M.R.H.Mandjes 'at' uva.nl
  • Dr. J.L. Dorsman, tel.: 020-5258209, e-mail: J.L.Dorsman 'at' uva.nl
  • N. Levering BSc., e-mail: nikki.levering 'at' student.uva.nl

Literature

The lectures are based on the following book:

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

Below, we will refer to this book as [AG]. It is recommended to have a copy, as we will continuously refer to this book. Next to the book, we may use additional handouts, which will be posted here.

Setup of the course and examination

This course will be taught by weekly lectures, in which all the material is treated. Please see datanose for the time and location of these lectures.

The examination of this course is in part comprised of four homework sets, each of which counts towards 10% of the final grade of the course. These will consist of both theoretical and practical (programming) exercises. There will also be an examination, which is an oral examination. The final grade for this course (FI) is based on the average homework grade (HW) and the exam grade (EX) in the following way: FI = 1{EX<5.0}EX + 1{EX>=5.0}(0.4HW+0.6EX).

Should this be required, there will be a retake opportunity for both components. When either of the components are `retaken', the final grade is computed again in accordance with the formula above taking into account all updated components, and the result will be 'final retake grade'. In particular, it is possible to only retake the homework or only retake the oral examination. The 'final retake grade' will then be computed using the original grade for the other component. A retake of both components is also possible.

This course is credited with 6 EC.

Homework and grades

The first homework set can be found here and is due on Monday Oct 8 at 9AM.
The second homework set can be found here and is due on Tuesday Oct 30 at 9AM.
The third homework set can be found here and is due on Tuesday Nov 27 at 9AM.
The fourth homework set can be found here and is due on Tuesday Dec 11 at 9AM.

All grades can be found on Canvas.

Schedule oral exam

The oral exams will take place on on Friday Dec 14 1PM-5PM and Tuesday Dec 18 9AM-1PM in room F3.37, SP107. The schedule can be found here.

Weekly program

The weekly program will be announced during the course of the course.
Week Date Lectured Material Slides
1 September 3 Introduction, [AG] Chapter I Slides week 1
2 September 10 [AG] Chapter II Slides week 2
3 September 17 Handout on discrete-event simulation
Mathematica file of simulation program		 Slides week 3
4 September 24 No lecture!
5 October 1 [AG] Chapter III Slides week 5
6 October 8 [AG] Chapter IV Slides week 6
7 October 15 [AG] V.2 and V.3 Slides week 7
8 October 22 [AG] V.4 and V.5 Slides week 8
9 October 30 [AG] V.6 and V.7 Slides week 9
10 November 6 [AG] V.8 and V.1 Slides week 10
11 November 13 [AG] Chapter VII Slides week 11
12 November 20 [AG] Chapter VI Slides week 12
13 November 27 [AG] Chapter VI Slides week 13