Data Science and Machine Learning (Theory and Projects) A to Z - Probability Model: Probability Models Conditional Indep

The video tutorial discusses the concept of dependent events in probability and how they can become conditionally independent when conditioned on a third event. It provides an example involving three events, A, B, and C, where A and B are initially dependent. The tutorial explains the probability conditions that define this dependency and demonstrates how conditioning on event C can lead to conditional independence between A and B.

What is the main focus of the initial section of the video?

• Introducing dependent events and their conditional independence

• Providing examples of independent events

• Explaining the concept of independent events

• Discussing the probability of event C

Which condition indicates that two events A and B are dependent?

• Probability of A and B equals the product of their individual probabilities

• Probability of A and B is not equal to the product of their individual probabilities

• Probability of A is greater than probability of B

• Probability of B is less than probability of A

What happens to events A and B when they are conditioned on a third event C?

• They remain dependent

• Their probabilities become equal

• They become conditionally independent

• They become more dependent

In the context of the video, what is the role of event C?

• It makes events A and B conditionally independent

• It makes events A and B independent

• It decreases the probability of event B

• It increases the probability of event A

What is the purpose of creating an example with events A, B, and C?

• To demonstrate conditional independence given event C

• To prove that C is dependent on A and B

• To illustrate that A and B are independent without C

• To show that A and B are always independent