## Math 60

### Honors Probability

 General Information Schedule Homework Assignments Links Exams

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Schedule

The following schedule is subject to change throughout the quarter.

Lecture Number Date Sections in Text Brief Description
1 3/26 1.1 Introduction, Examples of Problems, Intuition, Discrete vs. Continuous
2 3/31 1.2 Fundamental Definitions and Facts about discrete Random Variables
3 4/2 3.1 Basic Counting Techniques, Examples, Permutations
4 4/3 3.1, 3.2 More Counting Examples, Binomial Coefficents
5 4/4 3.2 Poker Hands, Binomial Theorem, Inclusion-Exclusion
6 4/7 3.2, 2.1 Hat-Check Problem, Stirling's Approximation, Continuous Random Variables
7 4/9 2.1, 2.2 Bertrand's Paradox, Density Functions, Cumulative Distribution Functions
8 4/11 2.2 Density Functions and Cumulative Distribution Functions, Examples
9 4/14 4.1 Discrete Conditional Probability, Independence of Events
10 4/16 4.1, 4.2 Independence of Random Variables, Continuous Conditional Probability
11 4/18 4.2, 5.1 Indenpendent Random Variables, Important Distributions
12 4/21 5.1, 5.2 Poisson Distribution, Normal Random Variables
13 4/23 5.2, 6.1 Normal Random Variables, Cauchy Density, Expected Value
14 4/25 6.1, 6.2 Expected Value and Variance of Discrete Random Variables
15 4/28 6.2, 6.3 Expected Value and Variance of Continuous Random Variables
16 4/30 7.1, 7.2 Sums of Discrete and Continuous Random Variables
17 5/2 8.1, 8.2 The Law of Large Numbers
18 5/5 11.1 Markov Chains
19 5/7 11.2 Absorbing Markov Chains
20 5/9 11.2, 11.3 Fundamental Theorems of Absorbing Markov Chains, Motivating Regular and Ergodic Markov Chains
21 5/12 11.4 Fundamental Theorems of Regular Markov Chains
22 5/14 11.5 Mean Recurrance Time and Mean First Passage Time
23 5/16 9.1 Central Limit Theorem for Bernoulli Trials
24 5/19 9.2, 9.3, 3.3 Central Limit Theorem, Card Shuffling
25 5/21 3.3 Card Shuffling
26 5/23 - Introduction to Measure Theory
17 5/28 - Measure Theory as a Foundation for Proabability Theory