Course

COM-516: Markov chains and algorithmic applications

Summary

The study of random walks finds many applications in computer science and communications. The goal of the course is to get familiar with the theory of random walks, and to get an overview of some applications of this theory to problems of interest in communications, computer and network science.

Lectures

Instructors (2)

Nicolas Macris

Nicolas Macris received the PhD degree in theoretical physics from EPFL and then pursued his scientific activity at the mathematics department of Rutgers University (NJ, USA). He then joined the Faculty of Basic Science of EPFL, working in the field of quantum statistical mechanics and mathematical aspects of the quantum Hall effect. Since 2005 he is with the Communication Theories Laboratory and Information Processing group of the School of Communication and Computer Science and currently works at the interface of statistical mechanics, information theory and error correcting codes, inference and learning theory. He held long-term visiting appointments and collaborations with the University College and the Institute of Advanced studies in Dublin, the Ecole Normale Supérieure de Lyon, the Centre de Physique Theorique Luminy Marseille, Paris XI Orsay, the ETH Zürich and more recently Los Alamos National Lab. CV and publication list.

Olivier Lévêque

Olivier Lévêque was born in Switzerland in 1971. He received the physics diploma from EPFL in 1995 and completed his PhD in mathematics at EPFL in 2001. Since then, he has been with the Laboratory of Information Theory at EPFL. He spent the academical year 2005-2006 at the Electrical Engineering Department of Stanford University, where he was appointed as lecturer. His research interests include stochastic analysis, random matrices, wireless communications and information theory.

Official source

Moodle Page

http://go.epfl.ch/COM-516

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Summary

Lectures

Instructors (2)

Nicolas Macris

Olivier Lévêque

Official source

Moodle Page

http://go.epfl.ch/COM-516

Lectures in this course (34)

Markov Chains: Definition and Examples

Covers the definition and properties of Markov chains, including transition matrix and examples.

Markov Chains: Transition Probabilities

Explores Markov chains, transition matrices, distribution, and random walks.

Markov Chains: States Classification

Explores the classification of states in Markov chains, emphasizing equivalence classes and periodicity.

Recurrence & Transience

Explores the concepts of recurrence and transience in Markov chains, distinguishing between them and discussing their implications.

Expected Number of Visits in State

Covers the criterion for recurrence in infinite chains based on the expected number of visits in a state.

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