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.
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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