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Probability for Statistics and Machine Learning

Overview of attention for book
Cover of 'Probability for Statistics and Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Review of Univariate Probability
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    Chapter 2 Multivariate Discrete Distributions
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    Chapter 3 Multidimensional Densities
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    Chapter 4 Advanced Distribution Theory
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    Chapter 5 Multivariate Normal and Related Distributions
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    Chapter 6 Finite Sample Theory of Order Statistics and Extremes
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    Chapter 7 Essential Asymptotics and Applications
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    Chapter 8 Characteristic Functions and Applications
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    Chapter 9 Asymptotics of Extremes and Order Statistics
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    Chapter 10 Markov Chains and Applications
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    Chapter 11 Random Walks
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    Chapter 12 Brownian Motion and Gaussian Processes
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    Chapter 13 Poisson Processes and Applications
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    Chapter 14 Discrete Time Martingales and Concentration Inequalities
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    Chapter 15 Probability Metrics
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    Chapter 16 Empirical Processes and VC Theory
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    Chapter 17 Large Deviations
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    Chapter 18 The Exponential Family and Statistical Applications
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    Chapter 19 Simulation and Markov Chain Monte Carlo
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    Chapter 20 Useful Tools for Statistics and Machine Learning
Attention for Chapter 7: Essential Asymptotics and Applications
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