Discrete Stochastic Processes Tools for Machine Learning and Data Science /
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 phas...
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| Natura: | Elettronico eBook |
| Lingua: | inglese |
| Pubblicazione: |
Cham :
Springer Nature Switzerland : Imprint: Springer,
2024.
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| Edizione: | 1st ed. 2024. |
| Serie: | Springer Undergraduate Mathematics Series,
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| Accesso online: | https://doi.org/10.1007/978-3-031-65820-4 |
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Sommario:
- - 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.