# Bayesian Networks MCQ Questions

11. What is needed to make probabilistic systems feasible in the world?

A. Reliability

B. Crucial robustness

C. Feasibility

D. None of the above

View Answer

Ans : B

Explanation: On a model-based knowledge provides the crucial robustness needed to make probabilistic system feasible in the real world.

12. Where does the bayes rule can be used?

A. Solving queries

B. Increasing complexity

C. Decreasing complexity

D. Answering probabilistic query

View Answer

Ans : D

Explanation: Bayes rule can be used to answer the probabilistic queries conditioned on one piece of evidence.

13. What does the bayesian network provides?

A. Complete description of the domain

B. Partial description of the domain

C. Complete description of the problem

D. None of the above

View Answer

Ans : A

Explanation: A Bayesian network provides a complete description of the domain.

14. ____________ is the process of calculating a probability distribution of interest e.g. P(A | B=True), or P(A,B|C, D=True).

A. Diagnostics

B. Supervised anomaly detection

C. Inference

D. Prediction

View Answer

Ans : C

Explanation: Inference is the process of calculating a probability distribution of interest e.g. P(A | B=True), or P(A,B|C, D=True)

15. The Distributive law simply means that if we want to marginalize out the variable A we can perform the calculations on the subset of distributions that contain A.

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

View Answer

Ans : A

Explanation: The Distributive law simply means that if we want to marginalize out the variable A we can perform the calculations on the subset of distributions that contain A

16. Bayesian networks are a factorized representation of the full joint.

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

View Answer

Ans : A

Explanation: Bayesian networks are a factorized representation of the full joint. (This just means that many of the values in the full joint can be computed from smaller distributions). This property used in conjunction with the distributive law enable Bayesian networks to query networks with thousands of nodes.

17. What is the consequence between a node and its predecessors while creating bayesian network?

A. Functionally dependent

B. Dependant

C. Conditionally independent

D. Both Conditionally dependant & Dependant

View Answer

Ans : C

Explanation: The semantics to derive a method for constructing bayesian networks were led to the consequence that a node can be conditionally independent of its predecessors.

18. Which condition is used to influence a variable directly by all the others?

A. Partially connected

B. Fully connected

C. Local connected

D. None of the above

View Answer

Ans : B

Explanation: Fully connected condition is used to influence a variable directly by all the others.

19. To which does the local structure is associated?

A. Hybrid

B. Dependant

C. Linear

D. None of the above

View Answer

Ans : C

Explanation: Local structure is usually associated with linear rather than exponential growth in complexity.

20. When we query a node in a Bayesian network, the result is often referred to as the marginal.

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

View Answer

Ans : A

Explanation: When we query a node in a Bayesian network, the result is often referred to as the marginal.

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