Zum Hauptinhalt springen Zur Suche springen Zur Hauptnavigation springen
Beschreibung
The third edition of this highly regarded text provides a rigorous, yet entertaining, introduction to probability theory and the analytic ideas and tools on which the modern theory relies. The main changes are the inclusion of the Gaussian isoperimetric inequality plus many improvements and clarifications throughout the text. With more than 750 exercises, it is ideal for first-year graduate students with a good grasp of undergraduate probability theory and analysis. Starting with results about independent random variables, the author introduces weak convergence of measures and its application to the central limit theorem, and infinitely divisible laws and their associated stochastic processes. Conditional expectation and martingales follow before the context shifts to infinite dimensions, where Gaussian measures and weak convergence of measures are studied. The remainder is devoted to the mutually beneficial connection between probability theory and partial differential equations, culminating in an explanation of the relationship of Brownian motion to classical potential theory.
The third edition of this highly regarded text provides a rigorous, yet entertaining, introduction to probability theory and the analytic ideas and tools on which the modern theory relies. The main changes are the inclusion of the Gaussian isoperimetric inequality plus many improvements and clarifications throughout the text. With more than 750 exercises, it is ideal for first-year graduate students with a good grasp of undergraduate probability theory and analysis. Starting with results about independent random variables, the author introduces weak convergence of measures and its application to the central limit theorem, and infinitely divisible laws and their associated stochastic processes. Conditional expectation and martingales follow before the context shifts to infinite dimensions, where Gaussian measures and weak convergence of measures are studied. The remainder is devoted to the mutually beneficial connection between probability theory and partial differential equations, culminating in an explanation of the relationship of Brownian motion to classical potential theory.
Über den Autor
Daniel W. Stroock is Simons Professor Emeritus of Mathematics at the Massachusetts Institute of Technology. He has published numerous articles and books, most recently 'Elements of Stochastic Calculus and Analysis' (2018) and 'Gaussian Measures in Finite and Infinite Dimensions' (2023).
Inhaltsverzeichnis
Notation; 1. Sums of independent random variables; 2. The central limit theorem; 3. Infinitely divisible laws; 4. Lévy processes; 5. Conditioning and martingales; 6. Some extensions and applications of martingale theory; 7. Continuous parameter martingales; 8. Gaussian measures on a Banach space; 9. Convergence of measures on a Polish space; 10. Wiener measure and partial differential equations; 11. Some classical potential theory; References; Index.
Details
Erscheinungsjahr: 2024
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Importe, Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
ISBN-13: 9781009549004
ISBN-10: 1009549006
Sprache: Englisch
Einband: Kartoniert / Broschiert
Autor: Stroock, Daniel W.
Hersteller: Cambridge University Press
Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße: 254 x 178 x 26 mm
Von/Mit: Daniel W. Stroock
Erscheinungsdatum: 14.10.2024
Gewicht: 0,873 kg
Artikel-ID: 130183077

Ähnliche Produkte