Torrent details for "Privault N. Discrete Stochastic Processes. Tools for ML and Data Science 2024 [andryold1]"    Log in to bookmark

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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.
Preface
List of Figures
A Summary of Markov Chains
Phase-Type Distributions
Synchronizing Automata
Random Walks and Recurrence
Cookie-Excited Random Walks
Convergence to Equilibrium
The Ising Model
Search Engines
Hidden Markov Model
Markov Decision Processes
Notes
Exercises
A Probability Generating Functions
A.1 Probability Generating Functions
Some Properties of Probability Generating Functions
B Some Useful Identities
References
Index
Author Index

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