£43.02

Springer Discrete Stochastic Processes: Tools for Machine Learning and Data Science (Springer Undergraduate Mathematics Series)

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Description

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.

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Format
paperback
Domain
Amazon UK
Release Date
08 October 2024
Listed Since
09 June 2024

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