An Introduction to Stochastic Processes: With Special Reference to Methods and Applications

additive process analysis approximate assumed asymptotic autoregressive average BARTLETT becomes Chapter characteristic function coefficients component condition consider continuous convenient convergence correlation correlogram corresponding covariance D. G. Kendall defined degrees of freedom denotes density function depend differential discrete distribution function dZ(w entropy equation equivalent estimate example finite formula frequency further given Hence independent individual infection integral interval Laplace transform likelihood function limiting linear process Markov chain Markov process matrix mean methods mutation non-zero normal obtain orthogonal P₁ particle particular periodogram point processes Poisson Poisson distribution population possible probability problem process X(t properties random variable recurrence relation relevant renewal result sampling sequence solution spectral spectral density spectrum stationary processes statistical stochastic processes t₁ theoretical theory time-series tion transition values variance vector whence X₁ zero σ²