Stochastic Modelling and Simulation

No.: 507074/507075, Semester: 8. S[860], Type: Lecture/Practical, Hours/Week: 2 L / 1 P

Lecturer
Ernst Stadlober / Ernst Stadlober

Status of the Course
Optional course of Technical Mathematics

Aims and objectives
Introduction to simulation methodology for problems in both mathematical statistics and systems simulation. To learn about the analysis of simulation output data.

Teaching method
Presentation of ideas on crude and sophisticated simulation stressing the strong interaction of applied mathematics, statistics and computing. Assignments are given to analyse small simulation models with computer aid (Maple, Matlab, Mathematica).

Contents
Modelling of Simple Stochastic Systems, Crude Simulation, Generation of Uniform Random Numbers, Random Variate Generation, Numerical and Graphical Description of Univariate Samples, Multifactorial Simulations with Case Studies, Bivariate Random Variables, Variance Reduction.

Pre-requisites
Probability Theory, Mathematical Statistics and Computational Statistics.

Teaching aids

Fishman, G. (1996), Monte Carlo, Concepts, Algorithms, and Applications, Springer, New York.
Fischman, g. (2001), Discrete Event Simulation, Modeling Programming, and Analysis, Springer, New York.
Lewis, P.A. and Orav, E.J. (1989), Simulation Methodology for Statisticians, Operations Analysts and Engineers, Wadsworth, Pacific Grove.
Ripley B.D. (1987): Stochastic Simulation, J. Wiley, New York.

Examination method
L: oral , P: case studies including written reports and oral presentations

Summer Semester 2002

    Tuesday, 9.00 - 10.00, Room 407         Wednesday, 9.00 - 10.30, Room 407

                                   Start: Tuesday, February 26, 9.00, room 407

There are 4 homeworks with 6 problems each. Each student should solve at least 8 problems.
The homeworks may be presented and discussed at the following days: