Y. Rozanov – författare

The study of random phenomena encountered in the real world is based on probability theory, mathematical statistics and the theory of random processes. The choice of the most suitable mathematical model is made on the basis of statistical data collected by observations. These models provide numerous tools for the analysis, prediction and, ultimately, control of random phenomena. The first part of the present volume (Chapters 1-3) can serve as a self-contained, elementary introduction to probability, random processes and statistics. It contains a number of relatively simple and typical examples of random phenomena which allow a natural introduction of general structures and methods. Some basic knowledge of elements of real/complex analysis, linear algebra and ordinary differential equations is required. The second part (Chapters 4-6) provides a foundation for stochastic analysis, gives information on basic models of random processes and tools to study them. A certain familiarity with elements of functional analysis is necessary. Important material is presented in the form of examples to keep readers involved.This is a concise textbook for a graduate-level course, with carefully selected topics representing the most important areas of modern probability, random processes and statistics.

This book considers some models described by means of partial differential equations and boundary conditions with chaotic stochastic disturbance. In a framework of stochastic partial differential equations an approach is suggested to generalize solutions of stochastic boundary problems. The main topic concerns probabilistic aspects with applications to the most well-known random fields models which are representative for the corresponding stochastic Sobolev spaces. This work assumes basic knowledge of general analysis and probability, such as Hilbert space methods, Schwartz distributions, and Fourier transforms. The volume should be of interest to researchers and postgraduate students whose work involves probability theory, stochastic processes and partial differential equations.

This book considers some models described by means of partial differential equations and boundary conditions with chaotic stochastic disturbance. In a framework of stochastic partial differential equations an approach is suggested to generalise solutions of stochastic boundary problems. The main topic concerns probabilistic aspects with applications to the most well-known random fields models which are representative for the corresponding stochastic Sobolev spaces. This work assumes basic knowledge of general analysis and probability, such as Hilbert space methods, Schwartz distributions, and Fourier transforms. Audience: This volume will be of interest to researchers and postgraduate students whose work involves probability theory, stochastic processes and partial differential equations.

Probability Theory, Theory of Random Processes and Mathematical Statistics are important areas of modern mathematics and its applications. They develop rigorous models for a proper treatment for various 'random' phenomena which we encounter in the real world. They provide us with numerous tools for an analysis, prediction and, ultimately, control of random phenomena. Statistics itself helps with choice of a proper mathematical model (e.g., by estimation of unknown parameters) on the basis of statistical data collected by observations. This volume is intended to be a concise textbook for a graduate level course, with carefully selected topics representing the most important areas of modern Probability, Random Processes and Statistics. The first part (Ch. 1-3) can serve as a self-contained, elementary introduction to Probability, Random Processes and Statistics. It contains a number of relatively sim­ ple and typical examples of random phenomena which allow a natural introduction of general structures and methods. Only knowledge of elements of real/complex analysis, linear algebra and ordinary differential equations is required here. The second part (Ch. 4-6) provides a foundation of Stochastic Analysis, gives information on basic models of random processes and tools to study them. Here a familiarity with elements of functional analysis is necessary. Our intention to make this course fast-moving made it necessary to present important material in a form of examples.