This chapter introduces the concept of a transition probability and the problem of guessing the input of an information channel from observing its output. It gives a first idea on the classical results of Shannon, without introducing the technicalities of stationary stochastic processes and the proof of Shanny’s Theorem. This material is provided in the next three chapters. Since it is not necessary for the understanding of Parts IV, V, and VI, one can move directly to Part IV after this chapter.
Error Probability Channel Capacity Information Channel Channel Output Bayesian Probability
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