Dr. Tim Brereton
Time and Place
Wednesday, 10 am - noon (220, Helmholtzstr. 18)
Friday, 10 am - noon (220, Helmholtzstr. 18)
Thursday, noon - 2 pm (220, Helmholtzstr. 18)
We agreed to start at 12:30 for being able to have lunch before. There might be exceptions around public holidays (check the news section).
4 hours lecture and 2 hours excercise
Credit points: 9
Basic knowledge of probability calculus and statistics as taught, for example, in "Elementare Wahrscheinlichkeitsrechnung und Statistik". In particular, the course Methods of Monte Carlo Simulation is not required.
Bachelor students in "Mathematik", "Wirtschaftsmathematik" and "Mathematische Biometrie"; Master students in "Finance"
Students from other fields (in particular Physics, Computer Science or Chemistry) are welcome as well; the respective examination board (Prüfungsausschuss) decides on the possible recognition of examinations.
This course is not a sequel to Methods of Monte Carlo Simulation , but rather a complimentary course. As such, MMCS1 is not a required prerequisite.
In this course, we focus on simulating probabilistic objects, including many important stochastic processes and structures. No prior knowledge of the probabilistic objects we study will be assumed. They will be introduced and some key properties will be examined. We also cover some basic results for measuring the accuracy of Monte Carlo estimates.
We will begin by considering random walks on graphs. We will then cover some basic theory about Markov chains that allows us to develop a number of simulation techniques. We will use these techniques to explore some spatial objects. We will look at some bounds on errors of various Monte Carlo estimators and also investigate some methods to improve efficiency when estimating various quantities related to stochastic processes and spatial objects. A number of real-world examples will be considered, mainly from physics and finance.
Requirements and Exam
In order to participate in the final exam, it is necessary to earn 50% of the points on all theory and 50% of the points on all programming exercises. Students who want to do so are kindly asked to register for the 'Vorleistung' in the LSF-'Hochschulportal'.
Time and place
Friday, July 31
from 10 am to noon in H14
Thursday, October 8
from 10 am to noon in room 1.20 (Helmholtzstr. 18)
The second exams are corrected!
You can find the number of points you obtained in the SLC in the section "Prüfungsleistung". The associated marks are indicated in the following table (it is the same for both exams):
|1,0||44 - 50|
|1,3||42 - 43,5|
|1,7||40 - 41,5|
|2,0||37,5 - 39,5|
|2,3||35,5 - 37|
|2,7||33,5 - 35|
|3,0||31,5 - 33|
|3,3||29 - 31|
|3,7||27 - 28,5|
|4,0||25 - 26,5|
|5,0||0 - 24,5|
The post-exam review will take place on Thursday and Friday, August 6+7 from 2 to 3 pm (each day) in Dr. Brereton's office (room 1.43 in Helmholtzstr. 18).
In order to receive points for your problem sheets, a registration at SLC is required.
Problem Sheet 01 Matlab Solution 01
Problem Sheet 02 Matlab Solution 02
Problem Sheet 03 Matlab Solution 03
Problem Sheet 04 Matlab Solution 04
Problem Sheet 05 Matlab Solution 05
Problem Sheet 06 Matlab Solution 06 Files: hiking.txt
Problem Sheet 07 Matlab Solution 07
Problem Sheet 08 Matlab Solution 08
Problem Sheet 09 Matlab Solution 09
Problem Sheet 10 Matlab Solution 10
Problem Sheet 11 Matlab Solution 11
Programming exercises have to be solved in Matlab. Student licenses can be bought for 20 € at O26/5101, or you can use it for free on many computers on campus, see this page for more information.
Lecture notes will be provided in this section, roughly one week after the corresponding lectures.
Asmussen, S. and P. Glynn. Stochastic Simulation. Springer, 2007.
Brémaud, P. Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues. Springer, 1999.
Dubhashi, D. P. and A. Panconesi. Concentration of Measure for the Analysis of Randomized Algorithms. Cambridge University Press, 2009.
Glasserman, P. Monte Carlo Methods in Financial Engineering. Springer, 2004.
Graham, C. and D. Talay. Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation. Springer, 2013.
Kroese, D. P., T. Taimre and Z. Botev. Handbook of Monte Carlo Methods. Wiley, 2011.
Levin, D. A., Y. Peres and E. L. Wilmer. Markov Chains and Mixing Times. American Mathematical Society, 2009.
Møller, J. and Waagepetersen, R. P. Statistical Inference and Simulation for Spatial Point Processes. Chapman & Hall/CRC, 2003.
Ross, S. M. Simulation, Fifth Edition. Academic Press, 2012.
Winkler, G. Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction. Springer, 2003.
We installed a mailing list to keep you up-to-date with important or short-term information regarding the course. You can subscribe on imap.uni-ulm.de/lists or by sending this email.
The name of the list is:
The second exams are corrected!
Please email your code as Matlab scripts, not as any other sort of text files and not in pdf format!
Lecture notes have been updated again.
On this page you can send us anonymous comments on the lectures and exercise lessons.