## A Basic Course in Probability Theory (2nd Edition)

**Author(s):**

Rabi Bhattacharya, Edward C. Waymire

**Publisher:**

Springer

**Pages:**265

**Further Actions:**

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### AVAILABLE FORMATS

Paperback - 9783319479729

21 February 2017

* $74.99*

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Ebook - 9783319479743

13 February 2017

* $59.99*

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This text develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wide-ranging applications. In this second edition, the text has been reorganized for didactic purposes, new exercises have been added and basic theory has been expanded. General Markov dependent sequences and their convergence to equilibrium is the subject of an entirely new chapter. The introduction of conditional expectation and conditional probability very early in the text maintains the pedagogic innovation of the first edition; conditional expectation is illustrated in detail in the context of an expanded treatment of martingales, the Markov property, and the strong Markov property. Weak convergence of probabilities on metric spaces and Brownian motion are two topics to highlight. A selection of large deviation and/or concentration inequalities ranging from those of Chebyshev, Cramer–Chernoff, Bahadur–Rao, to Hoeffding have been added, with illustrative comparisons of their use in practice. This also includes a treatment of the Berry–Esseen error estimate in the central limit theorem.The authors assume mathematical maturity at a graduate level; otherwise the book is suitable for students with varying levels of background in analysis and measure theory. For the reader who needs refreshers, theorems from analysis and measure theory used in the main text are provided in comprehensive appendices, along with their proofs, for ease of reference.Rabi Bhattacharya is Professor of Mathematics at the University of Arizona. Edward Waymire is Professor of Mathematics at Oregon State University. Both authors have co-authored numerous books, including a series of four upcoming graduate textbooks in stochastic processes with applications.

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Quicker paced introduction to the basics allows for a more in-depth treatment of such topics as convergence theory and Brownian motion

Self-contained and suitable for students with varying levels of background in analysis and measure theory

Includes a complete overview of basic measure theory and analysis (with proofs), and an extensive bibliography for further reading in the area

Written in a lively and engaging style

Second edition has additional exercises and expanded basic theory, and a new chapter on general Markov dependent sequences

Preface to First Edition

I. Random Maps, Distribution, and Mathematical Expectation

II. Independence, Conditional Expectation

III. Martingales and Stopping Times

IV. Classical Central Limit Theorems

V. Classical Zero-One Laws, Laws of Large Numbers and Large Deviations

VI. Fourier Series, Fourier Transform, and Characteristic Functions

VII. Weak Convergence of Probability Measures on Metric Spaces

VIII. Random Series of Independent Summands

IX. Kolmogorov's Extension Theorem and Brownian Motion

X. Brownian Motion: The LIL and Some Fine-Scale Properties

XI. Strong Markov Property, Skorokhod Embedding and Donsker's Invariance Principle

XII. A Historical Note on Brownian Motion

XIII. Some Elements of the Theory of Markov Processes and their Convergence to Equilibrium

A. Measure and Integration

B. Topology and Function Spaces

C. Hilbert Spaces and Applications in Measure Theory

References

Symbol Index

Subject Index.