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5.2 Joint Continuous Distributions Anna Karlin Most slides by Alex - PowerPoint PPT Presentation

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5.2 Joint Continuous Distributions Anna Karlin Most slides by Alex Tsun recap Joint PDFs (Example 1) R R Joint PDFs (Example 1) R R Joint PDFs (Example 1) R R Joint PDFs (Example 1) R R Joint PDFs (Example 1) R R Joint PDFs

  1. 5.2 Joint Continuous Distributions Anna Karlin Most slides by Alex Tsun

  2. recap

  3. Joint PDFs (Example 1) R R

  5. Joint PDFs (Example 1) R R

  6. Joint PDFs (Example 1) R R

  7. Joint PDFs (Example 1) R R

  8. Joint PDFs (Example 1) R R

  9. Joint PDFs (Example 1) R R

  10. Random Picture The “Normal” Distribution probability students Definition of The “Gaussian” Expectation Distribution

  11. Joint PDFs (Example 2)

  12. Joint PDFs (Example 2) 1 y=x 0

  13. Joint PDFs (Example 2) 1 y=x 0

  14. Joint PDFs (Example 2) 1 y=x 0

  15. Joint PDFs (Example 2) 1 y=x 0

  16. Joint PDFs (Example 2) 1 y=x 0

  17. Joint PDFs (Example 2) 1 y=x 0

  18. Joint PDFs (Example 2) 1 y=x 0

  19. Joint PDFs (Example 2) 1 y=x 0

  20. Joint PDFs (Example 2) 1 y=x 0

  21. Joint PDFs (Example 2) 1 y=x 0

  22. Joint PDFs (Example 2) 1 y=x 0

  23. Joint PDFs (Example 2) 1 y=x 0

  24. END PIC Alex Tsun Joshua Fan

  25. 5.3 Law of Total Expectation

  26. Agenda ● Conditional Expectation ● Law of Total Expectation (LTE)

  27. Conditional Expectation X E ( X | A ) = xPr ( X = x | A ) x ∈ Range ( X ) 65

  28. Law of Total Expectation 66

  29. Law of Total Expectation : Application System that fails in step i independently with probability p X # steps to fail E(X) ? Let A be the event that system fails in first step. 67

  30. Law of Total Expectation : Application System that fails in step i independently with probability p X # steps to fail E(X) ? Let A be the event that system fails in first step. E ( X ) = E ( X | A ) Pr ( A ) + E ( X | A ) Pr ( A ) = p + (1 + E ( X ))(1 − p ) = 1 + (1 − p ) E ( X ) E ( X ) = 1 68 p

  31. Linearity of expectation applies To conditional expectation too!! E(X+ Y | A) = E(X | A) + E(Y | A) E(aX + b | A)= a E(X | A) + b 69

  32. Law of total Expectation (RV version)

  33. Problem The number of people who enter an elevator on the ground floor is a Poisson random variable with mean 10. If there are N floors above the ground floor, and if each person is equally likely to get off at any one of the N floors, independently of where the others get off, compute the expected number of stops that the elevator will make before discharging all the passengers. 71

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