Required Text
This section contains the reading assignments from the course textbook:
Bertsekas, Dimitri P., and John N. Tsitsiklis. Introduction to Probability. Belmont, MA: Athena Scientific Press, June 2002. ISBN: 188652940X.
Recommended Texts
The following books cover many of the topics in this course, although in a different style. You may wish to consult them to get a different prospective on particular topics.
Drake, A. Fundamentals of Applied Probability Theory. New York, NY: McGraw-Hill, 1988. ISBN: 0070178151.
Ross, S. A First Course in Probability. Upper Saddle River, NJ: Prentice Hall, 2005. ISBN: 0131856626.
Readings by Session
Ses # | TOPICS | Readings |
---|---|---|
L1 | Probability Models and Axioms | Sections 1.1-1.2 |
L2 | Conditioning and Bayes' Rule | Sections 1.3-1.4 |
L3 | Independence | Section 1.5 |
L4 | Counting | Section 1.6 |
L5 | Discrete Random Variables; Probability Mass Functions; Expectations | Sections 2.1-2.4 |
L6 | Conditional Expectation; Examples | Sections 2.4-2.6 |
L7 | Multiple Discrete Random Variables | Section 2.7 |
L8 | Continuous Random Variables - I | Sections 3.1-3.3 |
L9 | Continuous Random Variables - II | Sections 3.4-3.5 |
L10 | Continuous Random Variables and Derived Distributions | Section 3.6 |
Quiz 1 (Covers up to Lec #1-8 Inclusive) | ||
L11 | More on Continuous Random Variables, Derived Distributions, Convolution | Section 4.2 |
L12 | Transforms | Section 4.1 |
L13 | Iterated Expectations | Sections 4.3 |
L13A | Sum of a Random Number of Random Variables | Section 4.4 |
L14 | Prediction; Covariance and Correlation | Sections 4.5-4.6 |
L15 | Weak Law of Large Numbers | Sections 7.1-7.3 |
Quiz 2 (Covers up to and Including Lec #14) | ||
L16 | Bernoulli Process | Section 5.1 |
L17 | Poisson Process | Section 5.2 |
L18 | Poisson Process Examples | Section 5.2 |
L19 | Markov Chains - I | Sections 6.1-6.2 |
L20 | Markov Chains - II | Section 6.3 |
L21 | Markov Chains - III | Section 6.4 |
L22 | Central Limit Theorem | Section 7.4 |
L23 | Central Limit Theorem (cont.), Strong Law of Large Numbers | Section 7.5 |
Final Exam |