Dynamical systems with random influences occur throughout the physical,
biological, and social sciences. By carefully studying a randomly
varying system over a small time interval, a discrete stochastic process
model can be constructed. Next, letting the time interval shrink to
zero, an Ito stochastic differential equation model for the dynamical
system is obtained.
This modeling procedure is thoroughly explained and illustrated for
randomly varying systems in population biology, chemistry, physics,
engineering, and finance. Introductory chapters present the fundamental
concepts of random variables, stochastic processes, stochastic
integration, and stochastic differential equations. These concepts are
explained in a Hilbert space setting which unifies and simplifies the
presentation. Computer programs, given throughout the text, are useful
in solving representative stochastic problems. Analytical and
computational exercises are provided in each chapter that complement the
material in the text.
Modeling with Itô Stochastic Differential Equations is useful for
researchers and graduate students. As a textbook for a graduate course,
prerequisites include probability theory, differential equations,
intermediate analysis, and some knowledge of scientific programming.