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A Basic Course in Probability Theory

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Cover of 'A Basic Course in Probability Theory'

Table of Contents

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    Book Overview
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    Chapter 1 Random Maps, Distribution, and Mathematical Expectation
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    Chapter 2 Independence, Conditional Expectation
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    Chapter 3 Martingales and Stopping Times
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    Chapter 4 Classical Central Limit Theorems
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    Chapter 5 Classical Zero–One Laws, Laws of Large Numbers and Large Deviations
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    Chapter 6 Fourier Series, Fourier Transform, and Characteristic Functions
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    Chapter 7 Weak Convergence of Probability Measures on Metric Spaces
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    Chapter 8 Random Series of Independent Summands
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    Chapter 9 Kolmogorov’s Extension Theorem and Brownian Motion
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    Chapter 10 Brownian Motion: The LIL and Some Fine-Scale Properties
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    Chapter 11 Strong Markov Property, Skorokhod Embedding, and Donsker’s Invariance Principle
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    Chapter 12 A Historical Note on Brownian Motion
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    Chapter 13 Some Elements of the Theory of Markov Processes and Their Convergence to Equilibrium
Attention for Chapter 2: Independence, Conditional Expectation
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