Discrete Stochastic Processes
Tools for Machine Learning and Data Science
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Beschreibung
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.
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.
Delves into the foundations of random processes, helping readers excel in machine learning and applied sciences Includes algorithms and codes for practice and illustration purposes Request lecturer material: sn.pub/lecturer-material
Autor*in
Nicolas Privault
Themen in »Discrete Stochastic Processes«
Markov Chains Random Walks Convergence and Mixing Search Engines Automata Hidden Markov Models Markov Decision Processes Reinforcement Learning Markov Chain Monte Carlo
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“The book is intended primarily for students preparing to engage in machine learning and data science. ... The book contains many exercises of varying difficulty. It provides and discusses in detail many useful facts about finite Markov chains and studies many well-known models and methods from an applied point of view. The book will be very useful in preparing for machine learning and data science.” (Alexander I. Zejfman, zbMATH 1571.60001, 2026)
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