Discrete Stochastic Processes

Tools for Machine Learning and Data Science

Autori

• Nicolas Privault

Parametri

Pagine

288pagine

Tempo di lettura

11ore

• Computer, IT, programmazione
• Matematica

Maggiori informazioni sul libro

Focusing on discrete-time stochastic processes, the text explores random interactions and algorithms centered on the Markov property. It delves into topics such as random walks, Markov chain convergence, and phase-type distributions, with practical applications in search engines and probabilistic automata. The introduction of the Ising model highlights its relevance in statistical physics. Additionally, it addresses data science applications through hidden Markov models and decision processes. The book includes 32 exercises and 17 detailed problems, enhancing understanding of statistical learning concepts.

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Lingua: Inglese

Pubblicato: 2024

(In brossura)