Introduction to Stochastic Processes - Lecture Notes

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Introduction to Stochastic Processes - Lecture Notes (with 33 illustrations)Gordan itkovi Department of MathematicsThe University of Texas at AustinContents1 Probability Random variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Countable sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . discrete random variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Expectation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Events and probability.

1.3 Discrete random variables A random variable is said to be discrete if it takes at most countably many values. More precisely, Xis said to be discrete if there exists a finite or countable set SˆR such that P[X2S] = 1, i.e., if we know with certainty that the only values Xcan take are those in S. The smallest set S

  Discrete, Stochastic