Probabilistic Systems Analysis and Applied Probability MIT

 Education

Video Lectures from 6.041 Probabilistic Systems Analysis and Applied Probability, Fall 2010

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Lecture 1: Probability Models and Axioms
In this lecture, the professor discussed probability as a mathematical framework, probabilistic models, axioms of probability, and gave some simple examples.

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Lecture 2: Conditioning and Bayes' Rule
In this lecture, the professor discussed conditional probability, multiplication rule, total probability theorem, and Bayes' rule.

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Lecture 3: Independence
In this lecture, the professor discussed independence of two events, independence of a collection of events, and independence vs. pairwise independence.

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Lecture 4: Counting
In this lecture, the professor discussed principles of counting, permutations, combinations, partitions, and binomial probabilities.

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Lecture 5: Discrete Random Variables I
In this lecture, the professor discussed random variables, probability mass function, expectation, and variance.

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Lecture 6: Discrete Random Variables II
In this lecture, the professor discussed conditional PMF, geometric PMF, total expectation theorem, and joint PMF of two random variables.
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