Courses:

Probabilistic Systems Analysis and Applied Probability >> Content Detail



Lecture Notes



Lecture Notes

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This section contains the lecture notes for the course. Each set of notes refer to reading assignments for the course textbook, Introduction to Probability. written by Professors John Tsitsiklis and Dimitri Bertsekas. Some of the slides in the notes are intentionally left blank, used by the instructors to work through material with students during class.


Ses #Topics
L1Probability Models and Axioms (PDF)
L2Conditioning and Bayes' Rule (PDF)
L3Independence (PDF)
L4Counting (PDF)
L5Discrete Random Variables; Probability Mass Functions; Expectations (PDF)
L6Conditional Expectation; Examples (PDF)
L7Multiple Discrete Random Variables (PDF)
L8Continuous Random Variables - I (PDF)
L9Continuous Random Variables - II (PDF)
L10Continuous Random Variables and Derived Distributions (PDF)
L11More on Continuous Random Variables, Derived Distributions, Convolution (PDF)
L12Transforms (PDF)
L13Iterated Expectations, Sum of a Random Number of Random Variables (PDF)
L14Prediction; Covariance and Correlation (PDF)
L15Bernoulli Process (PDF)
L16Poisson Process (PDF)
L17Poisson Process Examples (PDF)
L18Markov Chains - I (PDF)
L19Markov Chains - II (PDF)
L20Markov Chains - III (PDF)
L21Weak Law of Large Numbers (PDF)
L22Central Limit Theorem (PDF)
L23Strong Law of Large Numbers (PDF)
L24Interactive Exploration

 








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