Practice Learning in Graphical Models - 4.5 | 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 MLE stand for and why is it important in graphical models?

πŸ’‘ Hint: Think about what we need to do with the observed data.

Question 2

Easy

Name one method of learning parameters in graphical models.

πŸ’‘ Hint: Consider methods that include prior beliefs.

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 MLE estimate in graphical models?

  • A: The likelihood of data
  • B: Parameter values
  • C: The graph structure

πŸ’‘ Hint: Think about what MLE is concerned with.

Question 2

True or False: Structure learning is unnecessary if the graph structure is already known.

πŸ’‘ Hint: Consider when you would need to learn structures.

Solve 1 more question and get performance evaluation

Challenge Problems

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.

πŸ’‘ 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.

πŸ’‘ Hint: Think of how both methods provide information that can validate or disprove initial findings.

Challenge and get performance evaluation