## An Introduction to Vector Stochastic Processes |

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### Contents

Vector Linear Least Squares | 39 |

Vector Stochastic Processes | 71 |

Spectral representation | 87 |

Copyright | |

24 other sections not shown

### Common terms and phrases

associated assume Banach space becomes bounded Chapter characteristic complex components consider constant converges Corollary course covariance matrix cross-covariance matrix defined density function derivative determine differential equation differential system distribution equal establish example exists follows formula Gaussian given Hence identity implies increasing independent initial condition integral introduce Kalman least squares Lemma limit linear lognormal mean square mean square continuous mean zero n-dimensional nonnegative definite nonsingular norm normal numbers observation obtain operator orthogonal increments orthogonal matrix particular partition Pn+1 positive definite problem process with orthogonal Proof properties prove random process random vector Rayleigh representation respect result scalar Section sequence solution space spectral square matrix stationary Suppose Theorem tion unbiased estimate unique variable white noise write written x(to zero