Probability, Random Processes, and Ergodic Properties is for mathematically inclined information/communication theorists and people working in signal processing. It will also interest those working with random or stochastic processes, including mathematicians, statisticians, and economists.
Highlights:
Complete tour of book and guidelines for use given in Introduction, so readers can see at a glance the topics of interest.
Structures mathematics for an engineering audience, with emphasis on engineering applications.
New in the Second Edition:
Much of the material has been rearranged and revised for pedagogical reasons.
The original first chapter has been split in order to allow a more thorough treatment of basic probability before tackling random processes and dynamical systems.
The final chapter has been broken into two pieces to provide separate emphasis on process metrics and the ergodic decomposition of affine functionals.
Many classic inequalities are now incorporated into the text, along with proofs; and many citations have been added.
This book is a self-contained treatment of the theory of probability, random processes. It is intended to lay solid theoretical foundations for advanced probability, that is, for measure and integration theory, and to develop in depth the long term time average behavior of measurements made on random processes with general output alphabets. Unlike virtually all texts on the topic, considerable space is devoted to processes that violate the usual assumptions of stationarity and ergodicity, yet which still possess the fundamental properties of convergence of long term averages to appropriate expectations. The theory of asymtotically mean stationary processes and the ergodic decomposition are both treated in depth for both one-sided and two-sided random processes. In addition, the book treats many of the fundamental results such as the Kolmogorov extension theorem and the ergodic decomposition theorem. Much of the material has not previously appeared in book form, and the treatment takes advantage of many recent generalizations and simplifications.