25 episodes

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

Probabilistic Systems Analysis and Applied Probability MIT

    • Education
    • 4.5 • 23 Ratings

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

    • video
    Lecture 1: Probability Models and Axioms

    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.

    • 3 sec
    • video
    Lecture 2: Conditioning and Bayes' Rule

    Lecture 2: Conditioning and Bayes' Rule

    In this lecture, the professor discussed conditional probability, multiplication rule, total probability theorem, and Bayes' rule.

    • 3 sec
    • video
    Lecture 3: Independence

    Lecture 3: Independence

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

    • 2 sec
    • video
    Lecture 4: Counting

    Lecture 4: Counting

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

    • 3 sec
    • video
    Lecture 5: Discrete Random Variables I

    Lecture 5: Discrete Random Variables I

    In this lecture, the professor discussed random variables, probability mass function, expectation, and variance.

    • 3 sec
    • video
    Lecture 6: Discrete Random Variables II

    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.

    • 3 sec

Customer Reviews

4.5 out of 5
23 Ratings

23 Ratings

Lakhotia179 ,

Excellent

This is an excellent course. I am preparing for an exam in May 2013. This resource has proved to be a life saver. It's comprehensive, easy to understand and rigorous. Thank you so much OCW and professor John.

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