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 mentioned



 What does the Bayesian network provides? a) Complete description of the domain b) Partial de..

Answer / charu chauhan

Partial description of the domain:
Every Bayesian network provides a complete description of the domain and has a joint probability distribution: In order to construct a Bayesian network with the correct structure for the domain, we need to choose parents for each node such that this property holds.

Is This Answer Correct ?    7 Yes 3 No

Post New Answer

More AI Fuzzy Logic Interview Questions

There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory. a) Hedges b) Lingual Variable c) Fuzz Variable d) None of the mentioned

0 Answers  


What are advantages of fuzzy logic systems?

0 Answers  


The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ . a) Fuzzy Set b) Crisp Set

1 Answers  


What is fuzzy logic implementation?

0 Answers  


What is Fuzzy Logic? Where do You implement it?

3 Answers   iGate, T3 Softwares,






Traditional set theory is also known as Crisp Set theory. a) True b) False

1 Answers  


______________ is/are the way/s to represent uncertainty. a) Fuzzy Logic b) Probability c) Entropy d) All of the mentioned

0 Answers  


 What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables

0 Answers  


The truth values of traditional set theory is ____________ and that of fuzzy set is __________ a) Either 0 or 1, between 0 & 1 b) Between 0 & 1, either 0 or 1 c) Between 0 & 1, between 0 & 1 d) Either 0 or 1, either 0 or 1

1 Answers  


What is fuzzy logic?

0 Answers  


How many types of random variables are available? a) 1 b) 2 c) 3 d) 4

0 Answers  


 What is the consequence between a node and its predecessors while creating Bayesian network? a) Conditionally dependent b) Dependent c) Conditionally independent d) Both a & b

0 Answers  


Categories