Practice Key Concepts - 4.1.2 | 4. Graphical Models & Probabilistic Inference | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define conditional independence in your own words.

πŸ’‘ Hint: Think about how knowing one variable might not change your knowledge about another when a third is known.

Question 2

Easy

What is factorization in the context of probability distributions?

πŸ’‘ Hint: Consider how breaking down complex equations helps in calculations.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does conditional independence imply about two variables?

  • They are totally independent.
  • They are dependent only on a third variable.
  • They have no relationship.

πŸ’‘ Hint: Think about the role of a mediator.

Question 2

True or False: Factorization makes computations easier.

  • True
  • False

πŸ’‘ Hint: Consider why smaller pieces might be easier to handle.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a set of random variables A, B, and C with the independence relation A βŠ₯ B | C, construct a scenario where this condition holds and explain the implications.

πŸ’‘ Hint: Consider how knowing one aspect affects the connection between others.

Question 2

Using a graphical model, illustrate how different local structure alterations impact global outcomes in a probabilistic scenario.

πŸ’‘ Hint: Visual transformations can often simplify understanding complex interactions.

Challenge and get performance evaluation