Practice d-Separation in Bayesian Networks - 4.3.2 | 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

What does d-Separation help us determine?

πŸ’‘ Hint: Think about relationships and how they can be influenced.

Question 2

Easy

Define what a blocked path is in the context of Bayesian networks.

πŸ’‘ Hint: Consider what happens when a condition is placed on certain nodes.

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 d-Separation allow us to determine in a Bayesian network?

  • 1. Probabilistic independence
  • 2. Node connectivity
  • 3. Variable probabilities
  • 4. Path direction

πŸ’‘ Hint: Focus on independence among variables.

Question 2

In a collider structure, are A and C independent if B is not conditioned?

  • True
  • False

πŸ’‘ Hint: Evaluate the collider's impact on independence.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Consider a Bayesian network represented as A β†’ B ← C, D β†’ A. If B is conditioned but D is not, determine the independence of A and C. Justify your reasoning.

πŸ’‘ Hint: Analyze the paths through the collider's definition.

Question 2

Construct a Bayesian network diagram with four variables that demonstrate both blocked and unblocked paths. Explain your findings regarding independence.

πŸ’‘ Hint: Diagram it to clarify the paths!

Challenge and get performance evaluation