02979nam a22005295i 4500 978-3-031-65820-4 DE-He213 20251030113132.0 cr nn 008mamaa 241008s2024 sz | s |||| 0|eng d 9783031658204 978-3-031-65820-4 10.1007/978-3-031-65820-4 doi QA274-274.9 PBT bicssc PBWL bicssc MAT029040 bisacsh PBT thema PBWL thema Privault, Nicolas. author. aut http://id.loc.gov/vocabulary/relators/aut 85758 Discrete Stochastic Processes [electronic resource] : Tools for Machine Learning and Data Science / by Nicolas Privault. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XII, 288 p. 144 illus., 130 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Springer Undergraduate Mathematics Series, 2197-4144 - 1. A Summary of Markov Chains -- 2. Phase-Type Distributions -- 3. Synchronizing Automata -- 4. Random Walks and Recurrence -- 5. Cookie-Excited Random Walks -- 6. Convergence to Equilibrium -- 7. The Ising Model -- 8. Search Engines -- 9. Hidden Markov Model -- 10. Markov Decision Processes. 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. Stochastic processes. 76770 Computer science Mathematics. 64041 Stochastic Processes. 76770 Mathematical Applications in Computer Science. 65518 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031658198 Printed edition: 9783031658211 Springer Undergraduate Mathematics Series, 2197-4144 75368 https://doi.org/10.1007/978-3-031-65820-4 ZDB-2-SMA ZDB-2-SXMS 64065 64065