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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is approximate inference?
💡 Hint: Think about scenarios where calculations become too complex.
Question 2
Easy
Name one sampling method discussed in this section.
💡 Hint: Recall the sampling method that iteratively samples variables.
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 primary benefit of using approximate inference?
- It is always more accurate
- It handles complexities of high dimensions
- It is easier than exact inference
💡 Hint: Think about when exact methods fail.
Question 2
True or False: Variational Inference does not require optimization.
- True
- False
💡 Hint: Consider how this method approaches approximation.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a dataset with many categorical variables and want to apply Gibbs Sampling. How would you set up your sampling procedure?
💡 Hint: Focus on conditioning on the others while updating each variable.
Question 2
Assuming you implemented Metropolis-Hastings but noticed poor convergence. What adjustments could you consider to improve sample quality?
💡 Hint: Think about refining your proposal strategy to better explore the space.
Challenge and get performance evaluation