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Discrete stochastic processes = tools for machine learning and data science /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Discrete stochastic processes/ by Nicolas Privault.
其他題名:	tools for machine learning and data science /
作者:	Privault, Nicolas.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	xii, 288 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Stochastic processes. -
電子資源:	https://doi.org/10.1007/978-3-031-65820-4
ISBN:	9783031658204

Discrete stochastic processes = tools for machine learning and data science /
Privault, Nicolas.

Discrete stochastic processestools for machine learning and data science /[electronic resource] :by Nicolas Privault. - Cham :Springer Nature Switzerland :2024. - xii, 288 p. :ill. (some col.), digital ;24 cm. - Springer undergraduate mathematics series,2197-4144. - Springer undergraduate mathematics series..

- 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.

ISBN: 9783031658204

Standard No.: 10.1007/978-3-031-65820-4doiSubjects--Topical Terms:

528256
Stochastic processes.

LC Class. No.: QA274

Dewey Class. No.: 519.23

Discrete stochastic processes = tools for machine learning and data science /
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520 $a 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.
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