Probability and Probabilistic Reasoning for Electrical EngineeringThis text provides a comprehensive introduction to the mathematical theory of probability, its application to the modeling of random phenomena encountered in electrical and computer engineering, and its uses in making optimal decisions and inferences. Fine meets the needs of engineering students by addressing both highly conceptual mathematical methods and their real-world applications. He offers a sound introduction to the many elements of applied probability - the presentation is thorough, yet does not require a more advanced mathematical background beyond basic integral calculus. |
Contents
Introduction to Random Phenomena and Probability | 1 |
Background Events and Data | 25 |
10 | 49 |
Copyright | |
26 other sections not shown
Common terms and phrases
applications assume Bayes binary Boole's Inequality calculate cdf F Chapter characteristic function classical probability components conditional density conditional expectation conditional probability convergence convex combination correlation countably COV(X covariance matrix decision rule defined Definition denoted described determine discrete Evaluate event Example exponential finite follows Gaussian given graph H₁ Hence input integral interval Kalman filtering Kolmogorov axioms large numbers laws of large Lemma linear system loss function Markov mathematical mean square error mean square estimator multivariate normal nodes nonnegative observe occur outcomes output P₁ photons probabilistic probability measure Proof properties Provide an expression random experiment random phenomena random process random variables random vector real numbers relative frequency result sample space Section sequence signal specified Total Probability Theorem unlinked upper bound VAR(X variance voltage X₁ Y₁ yields zero