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.
Les mer
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.
Les mer
A rigorous, yet entertaining, account of the analytic foundations on which Kolmogorov built the theory of probability.
Produktdetaljer
ISBN
9781009549004
Publisert
2024-11-21
Utgave
3. utgave
Utgiver
Vendor
Cambridge University Press
Vekt
867 gr
Høyde
254 mm
Bredde
178 mm
Dybde
24 mm
Aldersnivå
G, 01
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
466
Forfatter