Probabilistic systems analysis: an introduction to probabilistic models, decisions, and applications of random processes
Elementary probability; Engineering applications of probability; Random variables; Expected values; Distribution of functions of Random variables; Applications of Random variables to systems problems; Distributions from data; Estimation; Engineering decisions; Introduction to Random processes; Systems and Random signals.
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ENGINEERING APPLICATIONS OF PROBABILITY
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action applications approximation assume assumption autocorrelation function average Bayes Bayesian Bernoulli trials called Chapter circuit coin components conditional expectation conditional probability consider decision problem 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 prior density probabilistic models probability density function probability mass function random process reliability resistance resistor Rxx(r sample function sample space selected shown in Figure signal spade standard deviation statistically independent subexperiments tossed transistor uniformly distributed variation Venn diagram voltage zero