1 / 24

5-1 Two Discrete Random Variables

5-1 Two Discrete Random Variables. Example 5-1.

Roberta
Download Presentation

5-1 Two Discrete Random Variables

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 5-1 Two Discrete Random Variables Example 5-1 Calls are made to check the airline schedule at your departure city. You monitor the number of bars of signal strength on your cell phone and the number of times you have to state the name of your departure city before the voice system recognize the name. Define: X = number of bars of signal strength on your cell phone Y = denote the number of times you need to state your departure city

  2. 5-1 Two Discrete Random Variables Figure 5-1Joint probability distribution of X and Y in Example 5-1.

  3. 5-1 Two Discrete Random Variables 5-1.1 Joint Probability Distributions

  4. 5-1 Two Discrete Random Variables 5-1.2 Marginal Probability Distributions • The individual probability distribution of a random variable is referred to as its marginal probability distribution. • In general, the marginal probability distribution of X can be determined from the joint probability distribution of X and other random variables. For example, to determine P(X =x), we sum P(X =x, Y =y) over all points in the range of (X, Y ) for which X =x. Subscripts on the probability mass functions distinguish between the random variables.

  5. 5-1 Two Discrete Random Variables Example 5-2

  6. 5-1 Two Discrete Random Variables Figure 5-2Marginal probability distributions of X and Y from Figure 5-1.

  7. 5-1 Two Discrete Random Variables Definition: Marginal Probability Mass Functions

  8. 5-1 Two Discrete Random Variables 5-1.3 Conditional Probability Distributions

  9. 5-1 Two Discrete Random Variables 5-1.3 Conditional Probability Distributions

  10. 5-1 Two Discrete Random Variables Definition: Conditional Mean and Variance

  11. Example 5-4 Figure 5-3Conditional probability distributions of Y given X,fY|x(y) in Example 5-4.

  12. 5-1 Two Discrete Random Variables 5-1.4 Independence Example 5-6

  13. Example 5-8 Figure 5-4(a)Joint and marginal probability distributions of X and Y in Example 5-8. (b) Conditional probability distribution of Y given X = x in Example 5-8.

  14. 5-1 Two Discrete Random Variables 5-1.4 Independence

  15. 5-1 Two Discrete Random Variables 5-1.5 Multiple Discrete Random Variables Definition: Joint Probability Mass Function

  16. 5-1 Two Discrete Random Variables 5-1.5 Multiple Discrete Random Variables Definition: Marginal Probability Mass Function

  17. 5-1 Two Discrete Random Variables Example 5-8 Figure 5-5Joint probability distribution of X1, X2, and X3.

  18. 5-1 Two Discrete Random Variables 5-1.5 Multiple Discrete Random Variables Mean and Variance from Joint Probability

  19. 5-1 Two Discrete Random Variables 5-1.5 Multiple Discrete Random Variables Distribution of a Subset of Random Variables

  20. 5-1 Two Discrete Random Variables 5-1.5 Multiple Discrete Random Variables Conditional Probability Distributions

  21. 5-1 Two Discrete Random Variables 5-1.6 Multinomial Probability Distribution

  22. 5-1 Two Discrete Random Variables 5-1.6 Multinomial Probability Distribution

More Related