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Probability essentials

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Cover of 'Probability essentials'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Axioms of Probability
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    Chapter 3 Conditional Probability and Independence
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    Chapter 4 Probabilities on a Finite or Countable Space
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    Chapter 5 Random Variables on a Countable Space
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    Chapter 6 Construction of a Probability Measure
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    Chapter 7 Construction of a Probability Measure on R
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    Chapter 8 Random Variables
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    Chapter 9 Integration with Respect to a Probability Measure
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    Chapter 10 Independent Random Variables
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    Chapter 11 Probability Distributions on R
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    Chapter 12 Probability Distributions on R n
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    Chapter 13 Characteristic Functions
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    Chapter 14 Properties of Characteristic Functions
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    Chapter 15 Sums of Independent Random Variables
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    Chapter 16 Gaussian Random Variables (The Normal and the Multivariate Normal Distributions)
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    Chapter 17 Convergence of Random Variables
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    Chapter 18 Weak Convergence
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    Chapter 19 Weak Convergence and Characteristic Functions
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    Chapter 20 The Laws of Large Numbers
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    Chapter 21 The Central Limit Theorem
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    Chapter 22 L 2 and Hilbert Spaces
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    Chapter 23 Conditional Expectation
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    Chapter 24 Martingales
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    Chapter 25 Supermartingales and Submartingales
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    Chapter 26 Martingale Inequalities
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    Chapter 27 Martingale Convergence Theorems
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    Chapter 28 The Radon-Nikodym Theorem
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Title
Probability essentials
Published by
Springer, January 2003
DOI 10.1007/978-3-642-55682-1
ISBNs
978-3-54-043871-7, 978-3-64-255682-1
Authors

Jacod, Jean, Protter, Philip E, Jean Jacod, Philip Protter, Protter, Philip

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The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 1 100%
Readers by discipline Count As %
Economics, Econometrics and Finance 1 100%