Artificial Intelligence MCQ Questions - Bayesian Networks

This section focuses on "Bayesian Networks" in Artificial Intelligence. These Multiple Choice Questions (MCQ) should be practiced to improve the AI skills required for various interviews (campus interviews, walk-in interviews, company interviews), placements, entrance exams and other competitive examinations.

1. Bayesian Belief Network is also known as ?

A. belief network
B. decision network
C. Bayesian model
D. All of the above

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2. Bayesian Network consist of ?

A. 2
B. 3
C. 4
D. 5

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3. The generalized form of Bayesian network that represents and solve decision problems under uncertain knowledge is known as an?

A. Directed Acyclic Graph
B. Table of conditional probabilities
C. Influence diagram
D. None of the above

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4. How many component does Bayesian network have?

A. 2
B. 3
C. 4
D. 5

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5. The Bayesian network graph does not contain any cyclic graph. Hence, it is known as a 

A. DCG
B. DAG
C. CAG
D. SAG

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6. In a Bayesian network variable is?

A. continuous 
B. discrete
C. Both A and B
D. None of the above

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7. If we have variables x1, x2, x3,....., xn, then the probabilities of a different combination of x1, x2, x3.. xn, are known as?

A. Table of conditional probabilities
B. Causal Component
C. Actual numbers
D. Joint probability distribution

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8. The nodes and links form the structure of the Bayesian network, and we call this the ?

A. structural specification
B. multi-variable nodes
C. Conditional Linear Gaussian distributions
D. None of the above

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9. Which of the following are used for modeling times series and sequences?

A. Decision graphs
B. Dynamic Bayesian networks
C. Value of information
D. Parameter tuning

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10. How many terms are required for building a bayes model?

A. 1
B. 2
C. 3
D. 4

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