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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does the E-step in the EM algorithm involve?
💡 Hint: Think about what is happening to the unobserved data.
Question 2
Easy
What is convergence in the context of the EM algorithm?
💡 Hint: Consider what it means for a method to 'settle down' after repeating it.
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 is the main purpose of the EM algorithm?
- Parameter estimation
- Data cleaning
- Feature selection
💡 Hint: What do you think it primarily tries to achieve with unseen variables?
Question 2
True or False: The M-step focuses on estimating the posterior probabilities.
- True
- False
💡 Hint: Remember what each step aims to accomplish.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with missing values, outline how you would apply the EM algorithm, detailing the steps involved.
💡 Hint: How would you approach the visibility of hidden data here?
Question 2
Critique a scenario where the EM algorithm fails to find an accurate model. What are the implications of local maxima in parameter estimation?
💡 Hint: What does this say about the starting point’s influence?
Challenge and get performance evaluation