Abstract
One focus of probability theory is distributions that are the result of an interplay of a large number of random impacts. Often a useful approximation can be obtained by taking a limit of such distributions, for example, a limit where the number of impacts goes to infinity. With the Poisson distribution, we have encountered such a limit distribution that occurs as the number of very rare events when the number of possibilities goes to infinity (see Theorem 3.7). In many cases, it is necessary to rescale the original distributions in order to capture the behavior of the essential fluctuations, e.g., in the central limit theorem. While these theorems work with real random variables, we will also see limit theorems where the random variables take values in more general spaces such as the space of continuous functions when we model the path of the random motion of a particle.
In this chapter, we provide the abstract framework for the investigation of convergence of measures. We introduce the notion of weak convergence of probability measures on general (mostly Polish) spaces and derive the fundamental properties. The reader will profit from a solid knowledge of point set topology. Thus we start with a short overview of some topological definitions and theorems.
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References
Billingsley P (1968) Convergence of probability measures. Wiley, New York
Billingsley P (1971) Weak convergence of measures: applications in probability. Society for Industrial and Applied Mathematics, Philadelphia, PA, 1971. Conference board of the mathematical sciences regional conference series in applied mathematics, No 5
Billingsley P (1999) Convergence of probability measures, 2nd edn. Wiley series in probability and statistics: probability and statistics. Wiley, New York. A Wiley-Interscience publication
Dudley RM (2002) Real analysis and probability. Cambridge studies in advanced mathematics, vol 74. Cambridge University Press, Cambridge. Revised reprint of the 1989 original
Dunford N, Schwartz JT (1958) Linear operators. I. General theory. With the assistance of WG Bade and RG Bartle. Pure and applied mathematics, vol 7. Interscience Publishers, New York
Elstrodt J (2011) Maß- und Integrationstheorie, 7th edn. Springer, New York
Kallenberg O (1986) Random measures, 4th edn. Akademie, Berlin
Kallenberg O (2002) Foundations of modern probability, 2nd edn. Probability and its applications. Springer, New York
Kelley JL (1975) General topology. Graduate texts in mathematics, vol 27. Springer, New York. Reprint of the 1955 edn. Van Nostrand, Toronto
Prohorov YuV (1956) Convergence of random processes and limit theorems in probability theory. Teor Veroâtn Ee Primen 1:177–238. Russian with English summary
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Klenke, A. (2014). Convergence of Measures. In: Probability Theory. Universitext. Springer, London. https://doi.org/10.1007/978-1-4471-5361-0_13
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DOI: https://doi.org/10.1007/978-1-4471-5361-0_13
Publisher Name: Springer, London
Print ISBN: 978-1-4471-5360-3
Online ISBN: 978-1-4471-5361-0
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