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170301  eng 
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a 9783319479743

100 
1 

a Bhattacharya, Rabi

245 
0 
0 
a A Basic Course in Probability Theory
h Elektronische Ressource
c by Rabi Bhattacharya, Edward C. Waymire

250 


a 2nd ed. 2016

260 


a Cham
b Springer International Publishing
c 2016, 2016

300 


a XII, 265 p
b online resource

505 
0 

a Preface to Second Edition  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 ZeroOne 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 FineScale 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

653 


a Measure theory

653 


a Probability Theory

653 


a Measure and Integration

653 


a Probabilities

700 
1 

a Waymire, Edward C.
e [author]

041 
0 
7 
a eng
2 ISO 6392

989 


b Springer
a Springer eBooks 2005

490 
0 

a Universitext

028 
5 
0 
a 10.1007/9783319479743

856 
4 
0 
u https://doi.org/10.1007/9783319479743?nosfx=y
x Verlag
3 Volltext

082 
0 

a 519.2

520 


a This text develops the necessary background in probability theory underlying diverse treatments of stochastic processes and their wideranging 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 coauthored numerous books, including a series of four upcoming graduate textbooks in stochastic processes with applications
