Stochastic Simulation

6 EC

Semester 1, period 2

5284STSI6Y

Owner Master Computational Science (joint degree)
Coordinator dr. A. Tabi
Part of Master Physics and Astronomy, track Theoretical Physics, Master Computational Science (Joint Degree), Master Physics and Astronomy (joint degree), track Biophysics and Biophotonics, Master Physics and Astronomy (joint degree), track General Physics and Astronomy,
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Course manual 2024/2025

Course content

The course builds on the mathematical foundations of probability theory while putting the emphasis on the tools used most often in computer simulations. Our main tool of study are the simulations themselves, therefore the study procedure includes a significant amount of programming. In particular we focus on the following topics:

  1. Elements of probability theory
  2. Random numbers
  3. Statistical analysis of data and error estimation
  4. Hypothesis testing and validation of simulation data
  5. Variance reduction techniques
  6. Discrete event simulations
  7. Queuing theory
  8. Random walks and Wiener process
  9. Monte Carlo and Metropolis methods
  10. Importance sampling
  11. Simulated annealing

Study materials

Literature

  • Sheldon M. Ross, 'Simulation', 5th edition

Syllabus

  • Reader

Objectives

  • To understand the foundations of probability theory and how it is applicable to various stochastic processes
  • To be able to construct and evaluate stochastic models to simulate various real-world systems from finance to biomedicine
  • To be able to perform an efficient simulation based on this model to capture the dynamics of key performance measures
  • To be able to analyze and test the validity of such models
  • To be able to interpret correctly the predictions of these stochastic models
  • To understand the theoretical foundations of different stochastic techniques and be able to apply them in practise

Teaching methods

  • Lecture
  • Computer lab session/practical training
  • Working independently on e.g. a project or thesis
  • Self-study

The lectures will present the theoretical background as well as adding several optional small simulation exercises. During these lectures three assignments will be defined that the students will work on in pairs. The guided laptop sessions will give aid with the technical questions towards the completion of the assignments.

Attendance

This programme does not have requirements concerning attendance (Ter part B).

Assessment

Item and weight Details

Final grade

15%

Assignment 1

Must be ≥ 5, NAP if missing

15%

Assignment 2

Must be ≥ 5, NAP if missing

20%

Assignment 3

Must be ≥ 5, NAP if missing

50%

Exam

Must be ≥ 5, NAP if missing

Assignments

The minimum of 50% of each assignment needs to be reached to be eligible for the exam. 

Students must work in a team of maximum three persons on these assignments. Together they hand in the report, which will be graded, and all of them receive the same grade for the assignment. Students must work in the same team for all three assignments.

Late submission policy for assignments: 1 day late: -0.5 points 2 days late: - 1 points 3 days late: -2 points 4 days or more days late: no grading

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

Contact information

Coordinator

  • dr. A. Tabi