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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does MLE stand for and why is it important in graphical models?
π‘ Hint: Think about what we need to do with the observed data.
Question 2
Easy
Name one method of learning parameters in graphical models.
π‘ Hint: Consider methods that include prior beliefs.
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What does MLE estimate in graphical models?
π‘ Hint: Think about what MLE is concerned with.
Question 2
True or False: Structure learning is unnecessary if the graph structure is already known.
π‘ Hint: Consider when you would need to learn structures.
Solve 1 more question and get performance evaluation
Push your limits with challenges.
Question 1
You have a dataset with prior distribution knowledge about parameters. Discuss how you would use Bayesian Estimation instead of MLE in this scenario.
π‘ Hint: Consider what prior distributions provide.
Question 2
Create an example of a learning scenario using both score-based and constraint-based methods, explaining how they would complement each other.
π‘ Hint: Think of how both methods provide information that can validate or disprove initial findings.
Challenge and get performance evaluation