## Probabilistic systems analysis: an introduction to probabilistic models, decisions, and applications of random processes |

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### Contents

INTRODUCTION | 1 |

ENGINEERING APPLICATIONS OF PROBABILITY | 47 |

EXPECTED VALUES | 106 |

Copyright | |

8 other sections not shown

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### Common terms and phrases

action applications approximation assume assumption autocorrelation function average Bayes Bayesian Bernoulli trials called Chapter circuit coin components conditional expectation conditional probability consider decision problem decision theory defective defined definition of probability derived described deterministic discussed elements empirical distribution function equally equation Example expected gain expected value experiment failure Fx(x given illustrate input joint probability jointly normal linear estimator loss marginal probability mean and autocorrelation mean and variance mean square error measure mutually exclusive noise normal distribution normal random variable Note observed outcomes output P(AB possible posterior density power density spectrum probabilistic models probability density function probability mass function random process reliability resistance resistor Rxx(t Rxxih sample function sample space selected shown in Figure signal spade standard deviation statistically independent subexperiments switch tossed transistor variation Venn diagram voltage zero