Practice Learning In Graphical Models (4.5) - Graphical Models & Probabilistic Inference
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Learning in Graphical Models

Practice - Learning in Graphical Models

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does MLE stand for and why is it important in graphical models?

💡 Hint: Think about what we need to do with the observed data.

Question 2 Easy

Name one method of learning parameters in graphical models.

💡 Hint: Consider methods that include prior beliefs.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does MLE estimate in graphical models?

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

💡 Hint: Think about what MLE is concerned with.

Question 2

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

💡 Hint: Consider when you would need to learn structures.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a dataset with prior distribution knowledge about parameters. Discuss how you would use Bayesian Estimation instead of MLE in this scenario.

💡 Hint: Consider what prior distributions provide.

Challenge 2 Hard

Create an example of a learning scenario using both score-based and constraint-based methods, explaining how they would complement each other.

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

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Reference links

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