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Topics in Probability

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Probability distributions Characteristic functions Analytic Characteristic Functions Infinitely Divisible Distributions Self-Decomposable Distributions Triangular Arrays as mentioned in this paper Theoretically, probability distributions are defined as
Abstract
Probability Distributions Characteristic Functions Analytic Characteristic Functions Infinitely Divisible Distributions Self-Decomposable Distributions Triangular Arrays.

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Book

Random Measures, Point Processes, and Stochastic Geometry

TL;DR: This book was designed to help researchers finding a direct path from the basic definitions and properties of these mathematical objects to their use in new and concrete stochastic models, with a main focus on random measures, point processes, and Stochastic geometry.
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Cramér–Von Mises distance estimation for some positive infinitely divisible parametric families with actuarial applications

TL;DR: Cramer-Von Mises (CVM) estimators as mentioned in this paper were developed for positive flexible infinitely divisible parametric families generalizing the compound Poisson family and are numerically implementable whenever the characteristic function has a closed form.
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Three triple integrals

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XXII.—Random Paths in Two and Three Dimensions.

TL;DR: In this article, a rectangular lattice is given and a particle P moves from one lattice-point to another in such a way that, when it is at any interior point, it is equally likely to move to any of its four neighbouring points.
Book

Probability Theory: An Introductory Course

TL;DR: Sinai's book as mentioned in this paper is a good introduction to probability theory for second or third year students inmathematics, physics or other natural sciences, and it could also be used by more advanced readers who want to learn themathematics of probability theory and some of its applications in statistical physics.
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On multiple returns in the random-walk problem

TL;DR: Chung and Good as discussed by the authors showed that the use of contour integrals allied with the method of steepest descents enables one to perform this calculation very simply, which is the purpose of the present paper.
Related Papers (5)