Probabilistic Systems Analysis and Applied Probability | Electrical Engineering and Computer Science | MIT OpenCourseWare

Course Info

Instructor

Prof. John Tsitsiklis

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2010

Level

Undergraduate

Topics

Learning Resource Types

Lecture Videos

Problem Sets

Problem Set Solutions

Exams

Exam Solutions

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Lecture Videos

Lecture 1: Probability Models and Axioms

Lecture 2: Conditioning and Bayes' Rule

Lecture 3: Independence

Lecture 4: Counting

Lecture 5: Discrete Random Variables; Probability Mass Functions; Expectations

Lecture 6: Discrete Random Variable Examples; Joint PMFs

Lecture 7: Multiple Discrete Random Variables: Expectations, Conditioning, Independence

Lecture 8: Continuous Random Variables

Lecture 9: Multiple Continuous Random Variables

Lecture 10: Continuous Bayes' Rule; Derived Distributions

Lecture 11: Derived Distributions; Convolution; Covariance and Correlation

Lecture 12: Iterated Expectations; Sum of a Random Number of Random Variables

Lecture 13: Bernoulli Process

Lecture 14: Poisson Process I

Lecture 15: Poisson Process II

Lecture 16: Markov Chains I

Lecture 17: Markov Chains II

Lecture 18: Markov Chains III

Lecture 19: Weak Law of Large Numbers

Lecture 20: Central Limit Theorem

Lecture 21: Bayesian Statistical Inference I

Lecture 22: Bayesian Statistical Inference II

Lecture 23: Classical Statistical Inference I

Lecture 24: Classical Inference II

Lecture 25: Classical Inference III; Course Overview

Course Info

Instructor

Prof. John Tsitsiklis

Departments

Electrical Engineering and Computer Science

As Taught In

Fall 2010

Level

Undergraduate

Topics

Learning Resource Types

Lecture Videos

Problem Sets

Problem Set Solutions

Exams

Exam Solutions

Download Course

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