Data Science and Machine Learning (Theory and Projects) A to Z - Random Variables: Geometric Random Variable Normalizati

The video explains geometric random variables, focusing on their infinite sample space and discreteness. It discusses the normalization property, proving that the sum of probabilities for all possible outcomes equals one. The video concludes with a preview of using Python to simulate geometric trials and plot distributions.

What is the feature or variable of interest when performing independent Bernoulli trials?

Explain why the sample space for a geometric random variable is considered infinite.

What does it mean for a random variable to be discrete, and how does this relate to geometric random variables?

Describe the relationship between the number of tosses and the outcome of a geometric random variable.

What is the significance of the probabilities of each value of a random variable being positive in the context of geometric random variables?

How does the normalization property apply to geometric random variables?

How can the sum of probabilities for a geometric random variable equal one despite having infinitely many outcomes?