# Probability Theory: Understanding Joint, Marginal, & Conditional Probability

Math    |    Intermediate
• 13 Videos | 1h 41m 47s
• Includes Assessment
Probability is all about estimating the likeliness of the occurrence of specific events. Use this course to learn more about defining and measuring joint, marginal, and conditional probabilities of events. Start by exploring the chain rule of probability and then use this rule to compute conditional probabilities of multiple events. You'll also investigate the steps involved in measuring the expected value of a random variable as the weighted sum of all outcomes, with each outcome weighted by its probability. By the time you finish this course, you will be able to compute joint, marginal, and conditional probabilities and the expected value of a random variable, as well as effectively utilize the chain rule of probability.

## WHAT YOU WILL LEARN

• discover the key concepts covered in this course define joint, marginal, and conditional probability link the definitions of marginal and conditional probability outline the chain rule of probability calculate joint probabilities associated with the rolling of a die compute marginal probabilities compute conditional probabilities
• simulate the rolling of two die to test joint probability calculate the joint probability of dependent variables calculate marginal and conditional probability on dependent variables define the formula of the expected value of a random variable compute the expected value of the rolling of a die summarize the key concepts covered in this course

## IN THIS COURSE

• 1.
Course Overview
• 2.
Joint, Marginal, and Conditional Probability
• 3.
Components of Marginal and Conditional Probability
• 4.
Chained Rule and Joint Probability of Events
• 5.
Calculating Marginal Probabilities
• 6.
Applying the Chain Rule to Conditional Probabilities
• 7.
Computing Joint Probabilities on Dice Rolls
• 8.
Exploring Joint Probability with Dependent Variables
• 9.
Computing Marginal and Conditional Probabilities with Dependent Variables
• 10.
Defining the Expected Value of a Random Variable
• 11.
Computing Expected Value of a Random Variable
• 12.
Computing Expected Value of a Dice Roll
• 13.
Course Summary

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