


Politecnico di Torino  
Academic Year 2017/18  
02KRRPE, 02KRRBG, 02KRRND, 02KRRNG Stochastic processes 

Master of sciencelevel of the Bologna process in Nanotechnologies For Icts  Torino/Grenoble/Losanna Master of sciencelevel of the Bologna process in Communications And Computer Networks Engineering  Torino Master of sciencelevel of the Bologna process in Energy And Nuclear Engineering  Torino Espandi... 





Subject fundamentals
The purpose of this course is to introduce the theory of the stochastic processes which are especially relevant in queueing systems, telematics networks, in telecommunication, and software engineering. The most relevant stochastic processes in both discrete and continuous time are described, together with a comprehensive list of examples of application. At the end of the course the student is expected to be able to formulate practical problems in mathematical terms and to calculate the quantity of interest either by the analytical methods or by simulations. The course include a summary of the most important notions of probability theory.

Expected learning outcomes
It is our goal to introduce the most basic mathematical and simulative tools to face problems where the evolution of a system is random, like for waiting times, first crossing times of threshold levels, or the number of failures in a given time of electronic devices. At the end of the course the students will be able to define and analyze simple stochastic models in queuing theory, network or software reliability, and from different areas of engineering. They will be able to solve them both through analytical and simulations, computing useful quantities like stationary distributions of systems, distribution of waiting times, mean times to reach absorbing states. They will be also able to understand which one of the presented processes is more appropriate in the analysis they have to perform, and the meaning of the values assumed by the mathematical objects presented during the lectures.
The ability to apply the gained knowledge will be verified through class exercises and analysis of simple case studies. 
Prerequisites / Assumed knowledge
A basic knowledge of calculus and a first course in probability theory are the prerequisites for this course. A minimum knowledge of basic probability will be assumed as previously acquired at the beginning of the course.

Contents
• Complements of basic probability: notable distributions, moment generating functions,conditional expectations, mixtures, order statistics – 6 hours.
• Poisson process: equivalent definitions, generalizations (nonhomogeneous, compound, mixed) and brief presentation of Renewal processes  10 hours. • Discrete time Markov chains: transition matrices, classification of states, stationarity and ergodicity, time reversibility, techniques for aggregation of states, branching processes  12 hours. • Continuous time Markov chains: transitions, birth and death processes, stationarity  9 hours • Brownian motions: definitions and main properties  3 hours. • Simulation with Matlab and analysis of some case studies (times to hit fixed thresholds for different kind of processes, probabilities of reaching absorbing states for analitically untractable processes, simulations of queueing systems) – 20 hours 
Delivery modes
In the first part of the course the lectures are held with the support of slides. Exercises are presented and solved in the class as well. In the second part of the course the lessons will mainly consist in process simulation with Mathlab, under the guidance of the teacher Technical discussions during class lectures will also help to assess the acquired level of knowledge and ability at the different stages of the course.

Texts, readings, handouts and other learning resources
Sheldon N. Ross Stochastic processes, ed John Wiley, any edition.
Slides of the lectures, exercises and examples of written exams, both with solutions, are available in the website of the course. 
Assessment and grading criteria
The exam consists of a written examination and a facultative oral examination. The written examination (2 hours) consists of 4 exercises, 3 of them similar to those presented during the lectures, where it is required to model some practical problems and to compute useful quantities like stationary distributions or expected times to reach specific states. One of the exercises consists on production of a Mathlab script that allows to simulate a process and to numerically provide useful quantities of interest in a practical engineering problem. The oral exam is possible under request for those students that in the written exam get a positive mark (greater or equal to 18/30).

