Discrete Stochastic Processes
Tools for Machine Learning and Data Science
Format:Paperback
Publisher:Springer International Publishing AG
Published:8th Oct '24
Should be back in stock very soon

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.
ISBN: 9783031658198
Dimensions: unknown
Weight: unknown
288 pages
2024 ed.