Practice Bayesian Networks (directed Graphical Models) (4.2.1) - Graphical Models & Probabilistic Inference
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Bayesian Networks (Directed Graphical Models)

Practice - Bayesian Networks (Directed Graphical Models)

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does a Bayesian Network represent?

💡 Hint: Think about what nodes and edges in a graph represent.

Question 2 Easy

Is a Bayesian Network a directed or undirected graph?

💡 Hint: Recall that the edges point in specific directions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What type of graph is used in Bayesian Networks?

Directed Acyclic Graph (DAG)
Undirected Graph
Cyclic Graph

💡 Hint: Remember, it's all about directionality and cycles.

Question 2

In Bayesian Networks, a node is conditionally independent of its non-descendants given its:

💡 Hint: Think about how nodes relate to each other in the graph.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Create a simple Bayesian Network for predicting the likelihood of rain based on humidity and temperature. Explain your node choices.

💡 Hint: Focus on the factors that directly influence the outcome you're predicting.

Challenge 2 Hard

Discuss the limitations of Bayesian Networks in representing real-world scenarios. Provide an example where they might struggle.

💡 Hint: Consider cases where many variables interact with each other simultaneously.

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