6.041SC | Fall 2013 | Undergraduate
Probabilistic Systems Analysis and Applied Probability

Unit I: Probability Models And Discrete Random Variables

« Previous | Next »

This unit covers the basic framework of probability theory: probabilistic models, conditional probabilities, independence, the Bayes’ rule, and counting methods. In addition, it introduces discrete random variables and the concept of the Probability Mass Function (PMF) used to describe the probability distribution of one or several random variables. Finally, it defines the concepts of expectation and variance, and their basic properties.

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

 Quiz 1

Looking for something specific in this course? The Resource Index compiles links to most course resources in a single page.

« Previous | Next »

Course Info
As Taught In
Fall 2013
Learning Resource Types
theaters Lecture Videos
theaters Recitation Videos
assignment_turned_in Problem Sets with Solutions
grading Exams with Solutions