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dc.contributor.authorKlenke, Achim
dc.date.accessioned2020-05-25T09:44:55Z
dc.date.available2020-05-25T09:44:55Z
dc.date.issued2014
dc.identifier.isbn978-1-4471-5361-0
dc.identifier.urihttp://ir.mksu.ac.ke/handle/123456780/6249
dc.description.abstractThis book is based on two four-hour courses on advanced probability theory that I have held in recent years at the universities of Cologne and Mainz. It is implicitly assumed that the reader has a certain familiarity with the basic concepts of probability theory, although the formal framework will be fully developed in this book. The aim of this book is to present the central objects and concepts of probability theory: random variables, independence, laws of large numbers and central limit theorems, martingales, exchangeability and infinite divisibility,Markov chains and Markov processes, as well as their connection with discrete potential theory, coupling, ergodic theory, Brownian motion and the Itô integral (including stochastic differential equations), the Poisson point process, percolation and the theory of large deviations. Measure theory and integration are necessary prerequisites for a systematic probability theory.We develop it only to the point to which it is needed for our purposes: construction of measures and integrals, the Radon–Nikodym theorem and regular conditional distributions, convergence theorems for functions (Lebesgue) and measures (Prohorov) and construction of measures in product spaces. The chapters on measure theory do not come as a block at the beginning (although they are written such that this would be possible; that is, independent of the probabilistic chapters) but are rather interlaced with probabilistic chapters that are designed to display the power of the abstract concepts in the more intuitive world of probability theory. For example, we study percolation theory at the point where we barely have measures, random variables and independence; not even the integral is needed. As the only exception, the systematic construction of independent random variables is deferred to Chapter 14. Although it is rather a matter of taste, I hope that this setup helps to motivate the reader throughout the measure-theoretical chapters. Those readers with a solid measure-theoretical education can skip in particular the first and fourth chapters and might wish only to look up this or that.en_US
dc.language.isoen_USen_US
dc.publisherSpringeren_US
dc.titleProbability Theoryen_US
dc.title.alternativeA Comprehensive Courseen_US
dc.typeBooken_US


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