A01=Jerzy Szulga
advanced probability theory
Author_Jerzy Szulga
Banach Space
Banach Steinhaus Theorem
Brownian Motion
Category=PBWL
Category=PBWS
chaos theory applications in science
Closed Graph
Closed Graph Theorems
Compensated Poisson Process
Conditional Expectations
Continuous Local Martingale
Decoupling
Decoupling Principle
Divergence
empirical statistics
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Fubini's Theorem
Fubini’s Theorem
graduate level mathematics
harmonic analysis
Hilbert Space
Hypercontraction
Lebesgue Measure
Lexicographic summation
Local Martingale
Localization
Locally Convex Space
Measure
nonlinear probability
Notation
Orlicz Space
Poisson Integral
Poisson Process
Polynomial Chaos
Positive Half Line
Probability
Random Measure
Random Variables
Recursion
Semimartingales
Standard Poisson Process
Stochastic Boundedness
stochastic processes
UDI spaces
Vector Space
Wiener Chaos
Wiener integral
Product details
- ISBN 9780412050916
- Weight: 521g
- Dimensions: 210 x 280mm
- Publication Date: 26 Mar 1998
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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Introduction to Random Chaos contains a wealth of information on this significant area, rooted in hypercontraction and harmonic analysis. Random chaos statistics extend the classical concept of empirical mean and variance. By focusing on the three models of Rademacher, Poisson, and Wiener chaos, this book shows how an iteration of a simple random principle leads to a nonlinear probability model- unifying seemingly separate types of chaos into a network of theorems, procedures, and applications.
The concepts and techniques connect diverse areas of probability, algebra, and analysis and enhance numerous links between many fields of science.
Introduction to Random Chaos serves researchers and graduate students in probability, analysis, statistics, physics, and applicable areas of science and technology.
Jerzy Szulga is Professor of Mathematics at Auburn University in Alabama, US.
