## Artificial Intelligence MCQ Questions - Bayesian Networks

Bayesian Networks MCQs : 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

View Answer

Ans : D

Explanation: Bayesian Belief Network also called a Bayes network, belief network, decision network, or Bayesian model.

2. Bayesian Network consist of ?

A. 2

B. 3

C. 4

D. 5

View Answer

Ans : A

Explanation: Bayesian Network can be used for building models from data and experts opinions, and it consists of two parts: Directed Acyclic Graph and Table of conditional probabilities.

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

View Answer

Ans : C

Explanation: The generalized form of Bayesian network that represents and solve decision problems under uncertain knowledge is known as an Influence diagram

4. How many component does Bayesian network have?

A. 2

B. 3

C. 4

D. 5

View Answer

Ans : A

Explanation: The Bayesian network has mainly two components: Causal Component and Actual numbers

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

View Answer

Ans : B

Explanation: The Bayesian network graph does not contain any cyclic graph. Hence, it is known as a directed acyclic graph or DAG.

6. In a Bayesian network variable is?

A. continuous

B. discrete

C. Both A and B

D. None of the above

View Answer

Ans : C

Explanation: Each node corresponds to the random variables, and a variable can be continuous or discrete.

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

View Answer

Ans : D

Explanation: If we have variables x1, x2, x3,....., xn, then the probabilities of a different combination of x1, x2, x3.. xn, are known as Joint probability distribution.

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

View Answer

Ans : A

Explanation: The nodes and links form the structure of the Bayesian network, and we call this the structural specification.

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

View Answer

Ans : B

Explanation: Dynamic Bayesian networks (DBNs) are used for modeling times series and sequences.

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

A. 1

B. 2

C. 3

D. 4

View Answer

Ans : C

Explanation: The three required terms are a conditional probability and two unconditional probability.

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