Practice Expectation-maximization (em) Algorithm (5.5) - Latent Variable & Mixture Models
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Expectation-Maximization (EM) Algorithm

Practice - Expectation-Maximization (EM) Algorithm

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What are the two main steps of the EM algorithm?

💡 Hint: Think about the actions performed in each step.

Question 2 Easy

Define a latent variable.

💡 Hint: Consider what is hidden or not visible.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does the E-step of the EM algorithm involve?

Maximizing log-likelihood
Estimating posterior probabilities
Updating model parameters

💡 Hint: Think about what the algorithm does in the first part.

Question 2

True or False: The EM algorithm guarantees finding the global maximum in optimization.

True
False

💡 Hint: Consider the nature of optimization.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Using the EM algorithm, suppose you need to analyze a dataset with missing values. Discuss how the E and M steps would be applied in your analysis.

💡 Hint: Think about how missing data influences the estimation process.

Challenge 2 Hard

Design a brief experiment to test different initialization parameters in the EM algorithm applied to a GMM. What outcomes would you expect and how would you analyze them?

💡 Hint: Experiment with different starting points for optimization.

Get performance evaluation

Reference links

Supplementary resources to enhance your learning experience.