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Welcome to the Uncertain Knowledge MCQs Page

Dive deep into the fascinating world of Uncertain Knowledge with our comprehensive set of Multiple-Choice Questions (MCQs). This page is dedicated to exploring the fundamental concepts and intricacies of Uncertain Knowledge, a crucial aspect of Artificial Intelligence. In this section, you will encounter a diverse range of MCQs that cover various aspects of Uncertain Knowledge, from the basic principles to advanced topics. Each question is thoughtfully crafted to challenge your knowledge and deepen your understanding of this critical subcategory within Artificial Intelligence.

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Check out the MCQs below to embark on an enriching journey through Uncertain Knowledge. Test your knowledge, expand your horizons, and solidify your grasp on this vital area of Artificial Intelligence.

Note: Each MCQ comes with multiple answer choices. Select the most appropriate option and test your understanding of Uncertain Knowledge. You can click on an option to test your knowledge before viewing the solution for a MCQ. Happy learning!

Uncertain Knowledge MCQs | Page 6 of 6

Q51.
How many terms are required for building a bayes model?

a.

1

b.

2

c.

3

d.

4

Discuss
Answer: (c).3
Discuss
Answer: (a).Complete description of the domain
Q53.
____________ is the process of calculating a probability distribution of interest e.g. P(A | B=True), or P(A,B|C, D=True).
Discuss
Answer: (c).Inference
Q54.
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.
Discuss
Answer: (a).True
Q55.
Bayesian networks are a factorized representation of the full joint.
Discuss
Answer: (a).True
Q56.
What is the consequence between a node and its predecessors while creating bayesian network?
Discuss
Answer: (c).Conditionally independent
Q57.
Which condition is used to influence a variable directly by all the others?
Discuss
Answer: (b).Fully connected
Q58.
To which does the local structure is associated?
Discuss
Answer: (c).Linear
Q59.
When we query a node in a Bayesian network, the result is often referred to as the marginal.
Discuss
Answer: (a).True
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