Monte Carlo og Diskret Hendelsesimulering

Monte Carlo and Discrete Event Simulation are two powerful classes of simulation methods that have a broad application range. Monte Carlo simulation is a set of stochastic algorithms for sampling, estimation and optimization, applied in a wide range of fields such as engineering, finance, computer science, and the physical and life sciences. An example application is the estimation / management of risk related to the cost or the schedule of a project. In Discrete Event Simulation, the time evolution of a system is modelled as a series of discrete events. Discrete event simulation can for example be used to find the bottleneck in a production line or to improve patient flow in a hospital. In this course, the student will learn to conduct simulation experiments with Monte Carlo methods and Discrete Event Simulation. All aspects of computer simulation will be covered: system-modelling, implementation of algorithms, running simulations, visualization and interpretation / analysis of results. Course topics include: • Random Number Generation. • Random Variates. • Random Processes. • Markov Chain Monte Carlo. • Queuing. • Statistical Analysis of Simulation Data. All topics are taught from low-level (theoretical understanding and low-level programming) to high-level (practical application to cases from industry and use of existing software). Relevant simulation examples will be given from different fields of science and industry.

Knowledge: - Be familiar with possible applications of Monte Carlo and Discrete Event Simulation. - Be able to explain in detail: • how one can sample random variates and random processes, • Markov chain Monte Carlo, • how one can simulate queuing, • methods for statistical analysis of simulation results. Skills: • To be able to conduct simulation experiments with Monte Carlo and Discrete Event methods, including system modelling, implementation of algorithms, running simulations, visualization, analysis and interpretation of results. General Competence: • Be able to reason which simulation method is best for a given system. • Be able to recognize and understand implementations of Monte Carlo and Discrete Event Simulation.

Lectures, video lectures, classroom exercises, 2-4 mandatory project assignments. The 2-4 mandatory project assignments have to be handed in and approved by the lecturer in order to get access to the exam.