Course manual 2021/2022

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:

Elements of probability theory
Random numbers
Statistical analysis of data and error estimation
Hypothesis testing and validation of simulation data
Variance reduction techniques
Discrete event simulations
Queuing theory
Random walks and Wiener process
Monte Carlo and Metropolis methods
Importance sampling
Simulated annealing

Study materials

Literature

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

Syllabus

The overview of the course will be given at the first lecture. The slides on the contents are available from Canvas.

Other

The course material is complemented by a set of relevant scientific papers.

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

Teaching methods

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

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.

Learning activities

Activity	Number of hours
Zelfstudie	168

Attendance

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

Assessment

Item and weight	Details
Final grade
0.5 (50%) Assignments	Must be ≥ 5
0.3 (30%) Assignment 1	Mandatory
0.3 (30%) Assignment 2	Mandatory
0.4 (40%) Assignment 3	Mandatory
0.5 (50%) Exam	Must be ≥ 5
1 (100%) Exam	Must be ≥ 5

Assignments

The description of the assessments and further supporting material is available from the Canvas site of the course.

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

Weeknummer	Onderwerpen	Studiestof
1
2
3
4
5
6
7
8

Owner	Master Computational Science (joint degree)
Coordinator	dr. G. Závodszky
Part of	Master Physics and Astronomy, track Physics of Life and Health, 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,

Stochastic Simulation