Practice Likelihood Function (probabilistic Models) (2.1.2) - Optimization Methods
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Likelihood Function (Probabilistic Models)

Practice - Likelihood Function (Probabilistic Models)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does the likelihood function represent?

💡 Hint: Think about what we want to evaluate regarding data and models.

Question 2 Easy

Define log-likelihood.

💡 Hint: Consider how logarithms are used to transform products into sums.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does the likelihood function measure?

Probability of parameters given the data
Probability of data given the parameters
Complexity of the model

💡 Hint: Focus on the direction of probability.

Question 2

True or False? The log-likelihood can be used to simplify the calculation process in maximum likelihood estimation.

True
False

💡 Hint: Consider how switching to logs affects multiplication.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a dataset with an equal number of binary outcomes (e.g., success and failure). Explain how you would use maximum likelihood estimation to quantify the success rate.

💡 Hint: Focus on calculating the proportion from your dataset.

Challenge 2 Hard

Given a scenario where you have multiple model candidates judging the same dataset, describe how you would use AIC and BIC to determine the best model.

💡 Hint: Remember to balance model complexity with fit!

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Reference links

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