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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dependence and independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conditional probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Examples.

While it is true that we do not know with certainty what value a random variable Xwill take, we usually know how to compute the probability that its value will be in some some subset of R. For example, we might be interested in P[X 7], P[X2[2;3:1]] or P[X2f1;2;3g]. The collection of all such probabilities is called the distribution of X.

  Value, Stochastic