Introduction to Stochastic ProcessesMarkov chains; Stationary distributions of a markov chain; Markov pure jump processes; Second order processes; Continuity, integration, and differentiation of second order processes; Stochastic differential equations, estimation theory, and spectral distribution. |
Other editions - View all
Introduction to Stochastic Processes Paul G. Hoel,Sidney C. Port,Charles J. Stone Limited preview - 1986 |
Introduction to Stochastic Processes Paul Gerhard Hoel,Sidney C. Port,Charles J. Stone No preview available - 1987 |
Common terms and phrases
absorbing balls birth and death branching called chain starting Chapter closed set compute conclude Consequently Consider constant continuous covariance function customers death chain death process defined density determine discussed distribution with parameter equals estimator Example Exercise expected Find finite follows formula Gaussian process given hence holds independent infinite jump leads Let X(t linear Markov chain mean mean and covariance nonnegative normally distributed observe obtain optimal order stationary process parameter particles particular period positive integer positive recurrent probability process X(t proof Pxy(t queuing chain random variables result rx(s rx(t satisfies second order process second order stationary Show shown solution space spectral stationary distribution stochastic differential equation stochastic process Suppose t₁ Theorem transient transition function values verify visits white noise X₁ Xo(t zero