Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Joint Distributions Solution

The video tutorial explains the derivation of the formula for the sum of two discrete random variables. It introduces the concept of a random variable Z as the sum of X and Y, and discusses how to find the expected value of Z using joint probability mass functions. The tutorial breaks down the formula into components, applies the double sum, and demonstrates how to derive marginal distributions. It concludes by showing that the expected value of the sum of two random variables is the sum of their expected values.

How can the expected value of X and Y be derived from their marginal distributions?

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What does it mean when we say that the expected value of the sum of two random variables is equal to the sum of their expected values?

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