02655nam 22005175 450 991089619380332120250808085220.03-031-65820-510.1007/978-3-031-65820-4(CKB)36315667800041(MiAaPQ)EBC31713218(Au-PeEL)EBL31713218(DE-He213)978-3-031-65820-4(EXLCZ)993631566780004120241008d2024 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierDiscrete Stochastic Processes Tools for Machine Learning and Data Science /by Nicolas Privault1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (294 pages)Springer Undergraduate Mathematics Series,2197-41443-031-65819-1 - 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.Springer Undergraduate Mathematics Series,2197-4144Stochastic processesComputer scienceMathematicsStochastic ProcessesMathematical Applications in Computer ScienceStochastic processes.Computer scienceMathematics.Stochastic Processes.Mathematical Applications in Computer Science.006.310727Privault Nicolas475313MiAaPQMiAaPQMiAaPQBOOK9910896193803321Discrete Stochastic Processes4212338UNINA