A nonmeasure theoretic introduction to stochastic processes. Considers its diverse range of applications and provides readers with probabilistic intuition and insight in thinking about problems. This revised edition contains additional material on compound Poisson random variables including an identity which can be used to efficiently compute moments; a new chapter on Poisson approximations; and coverage of the mean time spent in transient states as well as examples relating to the Gibb's sampler, the Metropolis algorithm and mean cover time in star graphs. Numerous exercises and problems have been added throughout the text.
J.M. Harrison . Brownian Motion and Stochastic Flow Systems , Wiley , New York ( 1985 ) . R.A. Howard . Dynamic Programming and Markov Processes , M.I.T. Press , Cambridge , MA ( 1960 ) . M. Iosifescu and P. Tautu . Stochastic ...
where ax denotes the common mean of the random variables X (t), —00 < t < 00. Since rx(s, t) depends only on the difference between s and t, (4) rx(s, t) = rx(0, t — s), —00 < s,t < 00. The function rx(t), —00 < t < 00, defined by rX(t) ...
The book is also an excellent reference for applied mathematicians and statisticians who are interested in a review of the topic.
Assuming only a backgroundin calculus, this outstanding text includes an introductionto basic stochastic processes.Reprint of the Prentice-Hall Publishers, Englewood Cliffs,New Jersey, 1975 edition.
This accessible introduction to the theory of stochastic processes emphasizes Levy processes and Markov processes.
This chapter also provides the solution of stochastic differential equations. This book will be of great value to mathematicians, engineers, and physicists.
This text offers easy access to this fundamental topic for many students of applied sciences at many levels. It includes examples, exercises, applications, and computational procedures.
This text on stochastic processes and their applications is based on a set of lectures given during the past several years at the University of California, Santa Barbara (UCSB).
Stochastic Processes in Dynamics
The random walk; Markov chains; Markov processes with discrete states in continuous time; Markov processes in continuous time with continuous state space; Non-markovian processes; Stationary processes: time domain; Stationary processes: ...