Practice D-separation In Bayesian Networks (4.3.2) - Graphical Models & Probabilistic Inference
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d-Separation in Bayesian Networks

Practice - d-Separation in Bayesian Networks

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does d-Separation help us determine?

💡 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.

💡 Hint: Consider what happens when a condition is placed on certain nodes.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

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

💡 Hint: Focus on independence among variables.

Question 2

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

True
False

💡 Hint: Evaluate the collider's impact on independence.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

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

💡 Hint: Analyze the paths through the collider's definition.

Challenge 2 Hard

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

💡 Hint: Diagram it to clarify the paths!

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Reference links

Supplementary resources to enhance your learning experience.