Continuous Parameter Markov Processes and Stochastic Differential Equations

This graduate text presents the elegant and profound theory of continuous parameter Markov processes and many of its applications.  The authors focus on developing context and intuition before formalizing the theory of each topic, illustrated with examples.

After a review of some background material, the reader is introduced to semigroup theory, including the HilleYosida Theorem,  used to construct continuous parameter Markov processes.  Illustrated with examples, it is a cornerstone of Fellers seminal theory of the most general one-dimensional diffusions studied in a later chapter. This is followed by two chapters with probabilistic constructions of jump Markov processes,  and   processes with independent increments, or Lévy processes. The greater part of the book is devoted to  Itôs fascinating theory of stochastic differential equations,  and to the study of  asymptotic properties of diffusions  in all dimensions, such as   explosion, transience, recurrence,  existence of steady states,  and the speed of convergence to equilibrium.  A broadly applicable functional central limit theorem for ergodic Markov processes is presented with important examples. Intimate connections between diffusions  and linear second order elliptic and parabolic partial differential equations are laid out in two chapters, and are used for computational purposes.  Among Special Topics chapters, two study anomalous diffusions: one on  skew Brownian motion, and the other on an intriguing multi-phase homogenization of solute transport in porous media.