One semester course in probability theory. We will cover the basic concepts of probability theory and show how the theory can be used in practice. The emphasis will be on examples and problem solving but reading and understanding proofs will also be an important part of the course.

Introduction to Probability,by Charles M. Grindstead and J. Laurie Snell. This book is available both in printed form and as a free PDF file here: Grinstead & Snell You will also find answers to the odd-numbered problems at that link.

There will be grades for quizzes and three midterm exams -- no final exam. All exams and quizzes are open book/notes, calculators allowed.

Quizzes 25 % Midterm Exams (25% each) 75 % For general policy statements about grades and academic honesty, go to: University Policy Statement.

Midterm I | Thursday, February 15 |

Midterm II | Thursday, March 29 |

Midterm III | Thursday, May 3 |

Homework will be assigned but not collected. Instead there will be weekly quizzes consisting of problems very similar to the homework problems. If you can do the homework problems you should have no trouble with the quizzes. Before each quiz there will be time to ask questions about the homework problems. One quiz score will be dropped to allow for an absence or just a "bad day." To see the assignments, click on the link above.

The text uses computer experiments as a fun way to illustrate some of the phenomena of probability theory. Mathematica versions of the programs referred to in the text can be found at the same webpage as the PDF text: Grinstead & Snell

I will be writing up some Mathematica notebooks for use in class. These can be found here: Mathematica Notebooks

You are encouraged to use Mathematica or some other program to do some explorations of your own. You can use Mathematica at the CSE computer labs. To get a CSE computer account go to CSE Accounts. Once you have a CSE lab account, you can also download a copy for your personal computer at: Get Mathematica

Week |
Topic |
Reading |

1/16-1/18 | Basic concepts of discrete probability | 1.1-1.2 |

1/23-1/25 | Continuous probability distributions and densities | 2.1-2.2 |

1/30-2/1 | Permutations and combinations | 3.1-3.2 |

2/6-2/8 | Discrete conditional probability, Bayes' formula | 4.1, 4.3 |

2/13-2/15 | Review, Midterm I | No new material |

2/20-2/22 | Some important distributions | 5.1-5.2 |

2/27-3/1 | Discrete Expected Value and Variance | 6.1-6.2 |

3/6-3/8 | Continuous Exp. and Var., Sums of Random Variables | 6.3,7.1,7,2 |

3/13-3/15 | Spring Break | |

3/20-3/22 | Law of Large Numbers, Central Limit I | 8.1, 8.2, 9.1 |

3/27-3/29 | Review, Midterm II | No new material |

4/3-4/5 | More Central Limit, Generating Functions | 9.2,9.3,10.1,10.3 |

4/10-4/12 | Markov Chains | 11.1-11.2 |

4/17-4/19 | More Markov Chains | 11.3-11.4 |

4/24-4/26 | Even more Markov, Gambler's Ruin | 11.5, 12.2 |

5/1-5/3 | Review, Midterm III | No new material |