Measure Theory, Probability, and Stochastic Processes

Name: Measure Theory, Probability, and Stochastic Processes
Brand: Springer, Berlin
SKU: 9783031142079
Price: 230.86 PLN
Availability: InStock

Autor: Jean-François Le Gall

Język:

Oprawa: Miękka

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy

Wysyłamy za 8-11 dni

230.86 zł

This textbook introduces readers to the fundamental notions of modern probability theory. The only p...

Informacje o książce

Autor

Jean-François Le Gall

Język

Angielski

Oprawa

Książka - Miękka

Data wydania

2023

strony

406

EAN

9783031142079

Enbook ID

44312731

Wydawca

Springer, Berlin

Waga

710

Wymiary

155 x 235

Pełny opis

This textbook introduces readers to the fundamental notions of modern probability theory. The only prerequisite is a working knowledge in real analysis. Highlighting the connections between martingales and Markov chains on one hand, and Brownian motion and harmonic functions on the other, this book provides an introduction to the rich interplay between probability and other areas of analysis.Arranged into three parts, the book begins with a rigorous treatment of measure theory, with applications to probability in mind. The second part of the book focuses on the basic concepts of probability theory such as random variables, independence, conditional expectation, and the different types of convergence of random variables. In the third part, in which all chapters can be read independently, the reader will encounter three important classes of stochastic processes: discrete-time martingales, countable state-space Markov chains, and Brownian motion. Each chapter ends with a selection of illuminating exercises of varying difficulty. Some basic facts from functional analysis, in particular on Hilbert and Banach spaces, are included in the appendix. Measure Theory, Probability, and Stochastic Processes is an ideal text for readers seeking a thorough understanding of basic probability theory. Students interested in learning more about Brownian motion, and other continuous-time stochastic processes, may continue reading the author's more advanced textbook in the same series (GTM 274).

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