Discrete Stochastic Processes

Tools for Machine Learning and Data Science

Nicolas Privault

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English

Springer International Publishing AG
08 October 2024

Probability & statistics; Stochastics; Mathematical theory of computation; Machine learning

Series: Springer Undergraduate Mathematics Series

This text presents selected applications of discrete-time stochastic processes that involve random interactions and algorithms, and revolve around the Markov property. It covers recurrence properties of (excited) random walks, convergence and mixing of Markov chains, distribution modeling using phase-type distributions, applications to search engines and probabilistic automata, and an introduction to the Ising model used in statistical physics. Applications to data science are also considered via hidden Markov models and Markov decision processes. A total of 32 exercises and 17 longer problems are provided with detailed solutions and cover various topics of interest, including statistical learning.

By: Nicolas Privault
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 2024 ed.
Dimensions: Height: 235mm, Width: 155mm,
ISBN: 9783031658198
ISBN 10: 3031658191
Series: Springer Undergraduate Mathematics Series
Pages: 288
Publication Date: 08 October 2024
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

Nicolas Privault received a PhD degree from the University of Paris VI, France. He was with the University of Evry, France, the University of La Rochelle, France, and the University of Poitiers, France. He is currently a Professor with the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. His research interests are in the areas of stochastic analysis and its applications.