Catene di Markov e applicazioni algoritmiche

Authors

Massiliano Goldwurm
University of Milan
ORCID logo https://orcid.org/0000-0003-0903-4699
Keywords: probabilistic algorithms, finite Markov chains, random generation, Markov Chain Monte Carlo methods

Synopsis

Markov chains represent a classical topic of probability theory that has a large number of applications in different areas, including mathematics, computer science, physics, biology, natural science, economics and several others. Typical examples of Markovian models concern for instance the analysis and interpretation of DNA sequences, voice recognition and the design of procedures for analysis and browsing of the web. In computer science and in particular in the algorithmic area Markov chains have been used to introduce the so-called Markov Chain Monte Carlo methods, which allow us to design probabilistic approximation algorithms for solving NP-hard problems. This work presents Markov chains and their algorithmic applications in a mathematical style, with a didactic spirit. It mainly has educational purposes, and it is specifically designed for teaching these topics in Italian university courses at a master level. 

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Author Biography

Massiliano Goldwurm, University of Milan

has obtained a PhD in Computer Science in 1988. Later he was researcher and associate professor in the same field. Since 2015 he has been full professor at the department of Mathematics of the State University of Milan. His research interests are in the field of Theoretical Computer Science and in particular in the areas of formal languages, analysis of algorithms and computational complexity. Main tools in this research activity are probabilistic and analytic methods. Since 1992 he has been teacher of several university courses at graduate and undergraduate level, concerning algorithms and data structures, probabilistic methods, computability and computational complexity. 

Published
January 31, 2024

Details about the available publication format: PDF

PDF
ISBN-13 (15)
979-12-5510-101-7