Probabilistic Models and Stochastic Programming

Abstract and Learning Objectives

The course covers the basic elements of i) Markovian models of stochastic systems and ii) Markovian decision processes as well as iii) basic elements of stochastic programming using scenario techniques. In this course, participants will learn how to construct and use these particular classes of probabilistic models of systems and decision processes or situations. The defining feature of the Markovian models is the memoryless property of the underlying stochastic processes. It essentially states that the future behavior of a system or decision process depends only on its current state, but not its previous history. The participants will learn why and how this often makes it possible to determine the probabilities of the different system states and how these probabilities can then be used to determine performance measures of the system or to assign economic values to decisions made in an uncertain environment.
 

Date of Event:

February 25-28, 2019
 

Location:

Leibniz Universität Hannover
Conti Campus
Königsworther Platz 1
30167 Hannover

Prof. Dr. Stefan Helber
Leibniz University Hannover
https://www.prod.uni-hannover.de/helber.html

Registration:

To get an overview of the amount of the participation fee and to register for the course, please use this link: http://vhbonline.org/veranstaltungen/prodok/anmeldung/
You can also send an email to prodok(at)vhbonline(dot)org.

Die Anmeldefrist ist abgelaufen.