Practice Dirichlet Process (DP) - 8.2 | 8. Non-Parametric Bayesian Methods | Advance Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define a Dirichlet Process in your own words.

πŸ’‘ Hint: Think about how it adapts to new data.

Question 2

Easy

What does the concentration parameter (Ξ±) in a Dirichlet Process influence?

πŸ’‘ Hint: Consider the effect of changing Ξ± on cluster count.

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 the formula G ~ DP(Ξ±, G0) represent?

  • A fixed number of parameters
  • An infinite mixture of distributions
  • A deterministic model

πŸ’‘ Hint: Remember what non-parametric means in this context.

Question 2

True or False: The Dirichlet Process can adapt the number of clusters as more data is observed.

  • True
  • False

πŸ’‘ Hint: Consider how different clustering might work with more data.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Suppose you have a dataset of animal features. How would you apply a Dirichlet Process for classification without prior knowledge of species numbers?

πŸ’‘ Hint: Consider how mixing behavior could illustrate your clusters.

Question 2

Critique the usage of a Dirichlet Process in the context of social media data classification. What challenges might arise?

πŸ’‘ Hint: Think about processing and determining meaningful clusters in streaming data.

Challenge and get performance evaluation