EECS 422: Random Processes in Communications and Control I

Winter 2014

Lecture 1 | Introduction, Probability spaces, properties of probability measures, conditional probability, statistical independence. |

Lecture 2 | Conditional independence, repeated trials, random variables, CDFs, PMFs, PDFs, mixed and singular random variables. |

Lecture 3 | Multiple random variables, independent RVs, conditioning and RVs, stochastic processes, the Bernoulli process. |

Lecture 4 | Expectations, functions of random variables, moment generating functions, conditional expectation. |

Lecture 5 | Markov Inequality, Chebyshev's inequality, Chernoff Bounds. |

Lecture 6 | Convergence of random variables, laws of large numbers, central limit theorem. |

Lecture 7 | Central Limit theorem cont'd., Poisson Processes. |

Lecture 8 | Poisson Processes cont'd. |

Lecture 9 | Markov Chains, transistion matrices/graphs, first step analysis. |

MIDTERM EXAM |

A list of lecture topics from 2013 can be found here.