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Aimed primarily at graduate students and researchers, this text is a comprehensive course in modern probability theory and its measure-theoretical foundations. It covers a wide variety of topics, many of which are not usually found in introductory textbooks. The theory is developed rigorously and in a self-contained way, with the chapters on measure theory interlaced with the probabilistic chapters in order to display the power of the abstract concepts in the world of probability theory. In addition, plenty of figures, computer simulations, biographic details of key mathematicians, and a wealth of examples support and enliven the presentation.…mehr

Produktbeschreibung
Aimed primarily at graduate students and researchers, this text is a comprehensive course in modern probability theory and its measure-theoretical foundations. It covers a wide variety of topics, many of which are not usually found in introductory textbooks. The theory is developed rigorously and in a self-contained way, with the chapters on measure theory interlaced with the probabilistic chapters in order to display the power of the abstract concepts in the world of probability theory. In addition, plenty of figures, computer simulations, biographic details of key mathematicians, and a wealth of examples support and enliven the presentation.


Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

  • Produktdetails
  • Verlag: Springer-Verlag GmbH
  • Seitenzahl: 621
  • Erscheinungstermin: 31. Dezember 2007
  • Englisch
  • ISBN-13: 9781848000483
  • Artikelnr.: 44131424
Autorenporträt
Achim Klenke is a professor at the Johannes Gutenberg University in Mainz, Germany.
Inhaltsangabe
Basic Measure Theory.- Independence.- Generating Functions.- The Integral.- Moments and Laws of Large Numbers.- Convergence Theorems.- Lp-Spaces and the Radon-Nikodym Theorem.- Conditional Expectations.- Martingales.- Optional Sampling Theorems.- Martingale Convergence Theorems and Their Applications.- Backwards Martingales and Exchangeability.- Convergence of Measures.- Probability Measures on Product Spaces.- Characteristic Functions and the Central Limit Theorem.- Infinitely Divisible Distributions.- Markov Chains.- Convergence of Markov Chains.- Markov Chains and Electrical Networks.- Ergodic Theory.- Brownian Motion.- Law of the Iterated Logarithm.- Large Deviations.- The Poisson Point Process.- The It o Integral.- Stochastic Differential Equations.
Rezensionen
From the book reviews:

"The book is dedicated to graduate students who start to learn probability theory as well as to those who need an excellent reference book. ... All results are presented in a self-contained way and are rigorously proved. Each section of the 26 chapters ends with a number of exercises, overall more than 270. ... Altogether it is a very valuable book for all students who specialize in probability theory or statistics." (Mathias Trabs, zbMATH, Vol. 1295, 2014)

"The book under review is a standard graduate textbook in this area of mathematics that collects various classical and modern topics in a friendly volume. ... the book contains many exercises. It is a very good source for a course in probability theory for advanced undergraduates and first-year graduate students. ... the book should be useful for a wide range of audiences, including students, instructors, and researchers from all branches of science who are dealing with random phenomena." (Mehdi Hassani, MAA Reviews, May, 2014)