## Random Processes: Noise, Optimum Filtering, Detection and Information Theories: An Intensive Course for Engineers, Scientists, and Mathematicians |

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### Contents

RANDOM PROCESSES | 2-2 |

EXAMPLES OF RANDOM PROCESSES | 3-3 |

FURTHER EXAMPLES OF PROBABILITY AND RANDOM PROCESSES | 3-13 |

18 other sections not shown

### Common terms and phrases

amplitude antenna applied approximately assumed available power average bandwidth based on likelihood called channel characteristic function coefficients complex compute condition consider constant correlation function corresponding decision problem decision rule decision theory defined definition denoted density function distribution function Equation equivalent example finite follows formula Fourier transform frequency Gaussian noise Gaussian process given hence Hilbert space independent integral interval likelihood ratio linear combination linear filter Markov Process mathematical matrix mean square error minimize noise power noise temperature observation obtained operator optimum filter output parameter phase errors possible prediction prob probability density probability distribution pulse radar radiation random process random variables receiver input resolution result sample sequence shot noise signal solution specified spectral density spectrum stationary statistics stochastic process Suppose theorem thermal tion transmitted twoport vector voltage wide-sense Markov Wiener xs(t zero