## An introduction to stochastic processes: with special references to methods and applications |

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### Contents

RANDOM SEQUENCES | 15 |

PROCESSES IN CONTINUOUS TIME | 45 |

MISCELLANEOUS STATISTICAL APPLICATIONS | 89 |

Copyright | |

7 other sections not shown

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### Common terms and phrases

additive process alternative analysis applications approximate assumed assumption asymptotic autoregressive average becomes Chapter characteristic function coefficients completely stationary component condition consider continuous convenient convergence correlation correlogram corresponding covariance cumulant function D. G. Kendall defined degrees of freedom denotes dependent differential discrete distribution function entropy epidemic equation equivalent ergodic estimates example exists exponential distribution formula frequency further given harmonic harmonic analysis Hence independent individual infection initial integral interval Kendall latent roots likelihood function linear process Markov chain Markov process mathematical matrix mean methods Moyal mutation negative binomial distribution non-zero normal observed obtain occurring orthogonal particle particular periodogram Poisson Poisson distribution population possible probability problem properties quantity random variable recurrence relation renewal result sampling sequence solution spectral spectrum stationary processes statistical stochastic processes theoretical theory time-series tion transition values variance vector whence zero