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This IEEE Press Classic Reissue provides an advanced level, yet uniquely fundamental, treatment of the applications of Statistical Communication Theory to a vast spectrum of important physical problems. Included are general treatments of signal detection, estimation, and measurement, and related topics involving information transfer. Using the Bayesian statistical viewpoint, renowned author David Middleton employs statistical decision theory specifically tailored for the general tasks of signal processing. Dr. Middleton also provides a special focus on physical modeling of the canonical channel with real-world examples relating to radar, sonar, and general telecommunications applications. This book offers a detailed treatment and an array of problems and results covering an exceptionally broad range of technical subjects in the communications field, including among others: Specific applications of Fourier as well as single- and two-sided LaPlace transform methods Evaluation of covariance functions and intensity spectra Signal-to-noise ratios in nonlinear systems Sampling and interpolation Langevin, Fokker-Planck, and Boltzmann equations Amplitude, phase, and frequency modulation by noise and signals Detection probabilities Optimum estimators Minimum detectable signals Neyman-Pearson and Ideal Observer detectionalgorithms Multiple alternative detection algorithms and performance measures Complete with special functions, integrals, solutions of integral equations, and an extensive, updated bibliography, An Introduction to Statistical Communication Theory is a seminal reference particularly for anyone working in the field of communications, as well as in other areas of statistical physics.