An Introduction to Stochastic Processes: With Special Reference to Methods and ApplicationsRandom sequences; Processes in continuous time; Miscellaneous statistical applications; Limiting stochastic operations; Stationary processes; Prediction and communication theory; The statistical analysis of stochastic processes; Correlation analysis of time-series. |
Contents
RANDOM SEQUENCES | 15 |
PROCESSES IN CONTINUOUS TIME | 49 |
MISCELLANEOUS STATISTICAL APPLICATIONS | 101 |
LIMITING STOCHASTIC OPERATIONS | 153 |
STATIONARY PROCESSES | 182 |
PREDICTION AND COMMUNICATION THEORY | 237 |
THE STATISTICAL ANALYSIS | 264 |
CORRELATION ANALYSIS OF TIMESERIES | 300 |
| 365 | |
| 381 | |
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Common terms and phrases
additive process analysis approximately 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 diffusion equation discrete 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 parameter particle particular periodogram point processes Poisson population possible probability problem process X(t properties random variable recurrence relation relevant renewal result roots sampling sequence solution spectral density spectral function spectrum stationary processes statistical stochastic processes t₁ theoretical theory tion transition values variance vector w₁ whence X₁ zero σ² дх