Ses # | TOPICS |
---|---|

L1 | Probability Models and Axioms (PDF) |

L2 | Conditioning and Bayes’ Rule (PDF) |

L3 | Independence (PDF) |

L4 | Counting Sections (PDF) |

L5 | Discrete Random Variables; Probability Mass Functions; Expectations (PDF) |

L6 | Conditional Expectation; Examples (PDF) |

L7 | Multiple Discrete Random Variables (PDF) |

L8 | Continuous Random Variables - I (PDF) |

L9 | Continuous Random Variables - II (PDF) |

L10 | Continuous Random Variables and Derived Distributions (PDF) |

L11 | More on Continuous Random Variables, Derived Distributions, Convolution (PDF) |

L12 | Transforms (PDF) |

L13 | Iterated Expectations (PDF) |

L13A | Sum of a Random Number of Random Variables (PDF) |

L14 | Prediction; Covariance and Correlation (PDF) |

L15 | Weak Law of Large Numbers (PDF) |

L16 | Bernoulli Process (PDF) |

L17 | Poisson Process (PDF) |

L18 | Poisson Process Examples (PDF) |

L19 | Markov Chains - I (PDF) |

L20 | Markov Chains - II (PDF) |

L21 | Markov Chains - III (PDF) |

L22 | Central Limit Theorem (PDF) |

L23 | Central Limit Theorem (cont.), Strong Law of Large Numbers (PDF) |

## Lecture Notes

## Course Info

##### Instructor

##### Departments

##### As Taught In

Spring
2006

##### Level

##### Learning Resource Types

*theaters*Simulation Videos

*laptop_windows*Simulations

*assignment_turned_in*Problem Sets with Solutions

*grading*Exams with Solutions

*notes*Lecture Notes