Stochastic Methods, Algorithms and Software Environments for Simulation, Modelling and Solving Problems in Science and Engineering
This course aims to introduce students to probability theory, discrete and continuous distributions, data presentation and fitting and relevant topics from distribution theory. It also introduces students to probabilistic techniques in Computer Science and Electronic Engineering as well as stochastic serial and parallel methods and algorithms such as Monte Carlo methods for scientific computation. The basic concepts of Monte Carlo methods will be considered. The generic properties making these methods very suitable for parallelisation will be outlined. Some examples of serial and parallel Monte Carlo algorithms for Matrix Computation, Solving Integrals and problems in the area of Optimization and Electronic Engineering will be presented.
Introduction to software environments for heterogeneous computing along with examples how they can be used for solving type of the problems described above will be made. Examples will be drawn from problems in Computer Science, Electronic Engineering, Computational Mathematics as well as Pollution Modelling, Information Retrieval and Data Mining, Financial and Market Modelling, etc.