Practice - Graphical Models & Probabilistic Inference
Practice Questions
Test your understanding with targeted questions
What do nodes and edges represent in a graphical model?
💡 Hint: Think about what connects the variables in the graph.
What is a Bayesian Network?
💡 Hint: Reflect on how the directionality plays a role in the model.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What do graphical models primarily represent?
💡 Hint: Consider what a graphical representation would entail in terms of probabilities.
Bayesian Networks are based on which type of graphs?
💡 Hint: Think about the directionality of edges in these models.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
Design a simple Bayesian Network for a basic weather forecasting model considering rain, humidity, and temperature. Define the conditional dependencies.
💡 Hint: Consider how one weather condition affects others.
Explain how you would use a Markov Random Field for image classification.
💡 Hint: Think about the influence of adjacent pixels in an image.
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Reference links
Supplementary resources to enhance your learning experience.