Course 02443: Stochastic Simulation, June 2023

Lecturer and instructor: Bo Friis Nielsen (bfni@dtu.dk)
Instructors: Emil S Larsen, Frederik M Rygaard, Nikolaj M Primault.

Textbook: Suggested reading material is:

  • Simulation, Sheldon M. Ross, Elsevier, 2013.
  • Simulation Modeling and Analysis, Søren Asmuussen and Peter W. Glyn Springer, 2015.
  • Introducing Monte Carlo Methods with R, C.P. Robert and G. Casella, Springer, 2010.
  • Numerisk Simulation(in Danish), Villy Bæk Iversen, DTU, 2007. (Learn).
  • Supplementary reading: Some other books I consult, copies of selected material available at DTU Inside

  • Simulation Modeling and Analysis, Averill M. Law, Mcgraw-hill, 2013 (5ed). Chapter 5 of edition 4 (Inside). (Learn).
  • Modern Simulation and Modeling, Reuven Y. Rubinstein and Benjamin Melamed, Wiley, 1998. (Inside). Selected material (Learn).
  • You do not necessarily have to study this wealth of material, indeed the course is experimental by nature, so ideally you should be able to solve exercises and problems by following the lectures and reading the slides. However, most students probably aim for more than that, particularly students who are already quite familiar with the basic concepts of probability and statistics. The course relies to a high degree on the Danish phrase ``Ansvar for egen læring'' (own responsibility of learning). In case you find the course too easy you are highly encouraged to dig deeper into the reading material and solve additional exercises from the text books.

    The course is planned as requiring full day activity. You should expect that the workday can last until 5pm. It is an experimental course, where learning by doing is the main driver. The lectures will give background and motivation while the exercises should give you the possibility to understand the material in more depth and give ability to work actively with concepts in simulation.

    Lectures will be held in Building 341 Auditorium 21 starting Thursday 1/6, 9.00. Lectures will continue until Friday 9/6.

    Computer exercises will be held in Building 358 Room 065, 066, and 067.

    The exercises are carried out in groups of two students. Groups named ex and numbered 1 through 161 are premade in DTU Learn. Once you have found a student to do the exercises with you register yourselves in DTU Learn. To ensure that the premade groups are assigned uniquely to students write also your group number along with at least one study number on the blackboard in Room 066 in Building 358. In special cases you can form a group of three students, e.g. if you are three students knowing each other and not knowing anybody else, or work alone if you are strong in the prerequisites and have special time constraints. You need confirmation from a teacher if you want to form a group of three or work for alone. There will be session at 1 pm Thursday June 1 in Room 067 in Building 358 for students who don't initially have somebody to work with. Students who have not formed a group by Friday afternoon (June 2nd) will be assigned automatically to a group.

    Each student is required to be able to demonstrate individual contribution and understanding of all questions. In particular is it not acceptable if students distribute the solution of questions to other groups. When help is needed it is the group that requires help rather than the individual student. The computer exercises can be found at the end of their associated slideshow. The computer exercises must be documented in reports and handed in Monday 12/6, preferably Friday 9/6. The report should document that the exercises have been solved. It is sufficient to provide extended lab notes, which is to be interpreted as computer code intertwined with plots and tables. It is important that the conclusions and lessons learned are added in text. Nice hand writing of this textual explanation is acceptable.

    Students who have not worked with statistics/programming for some years typically experience challenges with keeping up with the pace in the first part of the course. For many, however, the course contribute to a much better understanding of inferential statistics and sharpen programming skills that most likely will be needed for most once graduating. Some exercises have advanced material and students with weaker prerequisites can in some cases be exempted from doing the advanced material. You need to get such permission from one of the teachers. We register the permissions which is needed when correcting the exercises. For such students extension with the deadline for the handin of the exercises can be given.

    There will be a brush-up session in basic programming using R at 1.30pm Thursday June 1. It will mainly be based on questions and answers however we will go through basic concepts if desired.

    Simulation project 1 From Monday 12/6 until Wednesday 14/6, students will work on a somewhat longer project, that is quite specific. A report of this work must be handed in on Thursday 15/6. Team sizes can vary. However, teams of 4-5 students are considered most adequate. One straightforward way to form project groups is to merge two exercise groups.

    Simulation project 2 From Thursday 15/6 until Thursday 22/6, students will work on a topic selected among a few possibilities. Students who have problems that can be well analysed by simulation are encouraged to work on such problems after a small clarification with me (Bo Friis Nielsen) or the TA's. A report of this work must be handed in on Thursday 22/6 together with the report for Project 1. There is a possibility for an extension. Team sizes can vary. However, teams of 4-5 students are considered most adequate. One straightforward way to form groups for the second project is to keep the group for project 1.

    Any computer language implementing the most common probability and statistics functions (e.g. R, Matlab, Python) can be used. See e.g. R for Beginners by Emmanuel Paradis 76 pp.


    Course plan:


    The course plan is tentative. Updates and material will be provided. Some adjustments are possible.


    The following material is from June 2022.

    Last change: 12/6 2023, by bfn