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One Random Variable

One Random Variable. Random Process. The Cumulative Distribution Function. We have already known that the probability mass function of a discrete random variable is The cumulative distribution function is an alternative approach, that is

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One Random Variable

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  1. One Random Variable Random Process

  2. The Cumulative Distribution Function • We have already known that the probability mass function of a discrete random variable is • The cumulative distribution function is an alternative approach, that is • The most important thing is that the cumulative distribution function is not limited to discrete random variables, it applies to all types of random variables • Formal definition of random variable Consider a random experiment with sample space S and event class F. A random variable X is a function from the sample space S to R with the property that the set is in F for every b in R

  3. The Cumulative Distribution Function • The cumulative distribution function (cdf) of a random variable X is defined as • The cdf is a convenient way of specifying the probability of all semi-infinite intervals of the real line (-∞, b]

  4. Example 1 • From last lecture’s example we know that the number of heads in three tosses of a fair coin takes the values of 0, 1, 2, and 3 with probabilities of 1/8, 3/8, 3/8, and 1/8 respectively • The cdf is the sum of the probabilities of the outcomes from {0, 1, 2, 3} that are less than or equal to x

  5. Example 2 • The waiting time X of a costumer at a taxi stand is zero if the costumer finds a taxi parked at the stand • It is a uniformly distributed random length of time in the interval [0, 1] hours if no taxi is found upon arrival • Assume that the probability that a taxi is at the stand when the costumer arrives is p • The cdf can be obtained as follows

  6. The Cumulative Distribution Function • The cdf has the following properties:

  7. Example 3 • Let X be the number of heads in three tosses of a fair coin • The probability of event can be obtained by using property (vi) • The probability of event can be obtained by realizing that the cdf is continuous at and

  8. Example 3 (Cont’d) • The cdf for event can be obtained by getting first By using property (vii)

  9. Types of Random Variable • Discrete random variables: have a cdf that is a right-continuous staircase function of x, with jumps at a countable set of points • Continuous random variable: a random variable whose cdf is continuous everywhere, and sufficiently smooth that it can be written as an integral of some nonnegative function

  10. Types of Random Variable • Random variable of mixed type: random variable with a cdf that has jumps on a countable set of points, but also increases continuously over ar least one interval of values of x where , is the cdf of a discrete random variable, and is the cdf of a continuous random variable

  11. The Probability Density Function • The probability density function (pdf) is defined as • The properties of pdf

  12. The Probability Density Function

  13. The Probability Density Function • A valid pdf can be formed from any nonnegative, piecewise continuous function that has a finite integral • If , the function will be normalized

  14. Example 4 • The pdf of the uniform random variable is given by • The cdf will be

  15. Example 5 • The pdf of the samples of the amplitude of speech waveform is decaying exponentially at a rate α • In general we define it as • The constant, c can be determined by using normalization condition as follows • Therefore, we have • We can also find

  16. Pdf of Discrete Random Variable • Remember these: Unit step function • The pdf for a discrete random variable is

  17. Example 6 • Let X be the number of head in three coin tosses • The cdf of X is • Thus, the pdf is • We can also find several probabilities as follows

  18. Conditional Cdf’s and Pdf’s • The conditional cdf of X given C is • The conditional pdf of X given C is

  19. The Expected Value of X • The expected value or mean of a random variable X is • Let Y = g(X), then the expected value of Y is • The variance and standard deviation of the random variable X are

  20. The Expected Value of X • The properties of variance • The n-th moment of the random variable is

  21. Some Continuous Random Variable

  22. Some Continuous Random Variable

  23. Some Continuous Random Variable

  24. Some Continuous Random Variable

  25. Some Continuous Random Variable

  26. Transform Methods • Remember that when we perform convolution between two continuous signal , we can perform it in another way • First we do transformation (that is, Fourier transform), so that we have

  27. Transform Methods • The characteristic function of a random variable X is • The inversion formula that represent pdf is

  28. Example 7: Exponential Random Variable

  29. Transform Methods • If we subtitute into the formula of yields • When the random variables are integer-valued, the characteristic function is called Fourier transform of the sequence as follows • The inverse:

  30. Example 8: Geometric Random Variable

  31. Transform Methods • The moment theorem states that the moments of X are given by

  32. Example 9

  33. The Probability Generating Function • The probability generating function of a nonnegative integer-valued random variable N is defined by • The pmf of N is given by

  34. The Laplace Transform of The Pdf • The Laplace transform of the pdf can be written as • The moment theorem also holds

  35. Example 10

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