Multiparameter Processes

Multi-parameter processes extend the existing one-parameter theory in an elegant way and have many applications to other fields in mathematics such as real analysis, functional analysis, group theory, and analytic number theory, to name a few. This book on the vast and rapidly developing subject of random fields is designed for a second graduate course in probability. Recent work on random fields has made it possible to make it an expository subject which interacts with several other areas in mathematics and has enough mathematical depth to be of use to pure as well as applied mathematicians of many backgrounds.

Includes supplementary material: sn.pub/extras

Klappentext
Multiparameter processes extend the existing one-parameter theory of random processes in an elegant way, and have found connections to diverse disciplines such as probability theory, real and functional analysis, group theory, analytic number theory, and group renormalization in mathematical physics, to name a few.

This book lays the foundation of aspects of the rapidly-developing subject of random fields, and is designed for a second graduate course in probability and beyond. Its intended audience is pure, as well as applied, mathematicians.

Davar Khoshnevisan is Professor of Mathematics at the University of Utah. His research involves random fields, probabilistic potential theory, and stochastic analysis.

Inhalt
Discrete-Parameter Random Fields.- Discrete-Parameter Martingales.- Two Applications in Analysis.- Random Walks.- Multiparameter Walks.- Gaussian Random Variables.- Limit Theorems.- Continuous-Parameter Random Fields.- Continuous-Parameter Martingales.- Constructing Markov Processes.- Generation of Markov Processes.- Probabilistic Potential Theory.- Multiparameter Markov Processes.- The Brownian Sheet and Potential Theory.