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
Answer Posted / 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 View All Answers
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 mentioned
Where do we implement artificial intelligence fuzzy logic?
What are disadvantages of fuzzy logic systems?
What is fuzzy logic systems architecture?
How many types of random variables are available? a) 1 b) 2 c) 3 d) 4
Which of the following is used for probability theory sentences? a) Conditional logic b) Logic c) Extension of propositional logic d) None of the mentioned
Like relational databases there does exists fuzzy relational databases. a) True b) False
What are the advantages of fuzzy logic systems?
Fuzzy logic is a form of a) Two-valued logic b) Crisp set logic c) Many-valued logic d) Binary set logic
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
____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic. a) Fuzzy Relational DB b) Ecorithms c) Fuzzy Set d) None of the mentioned
What is fuzzy logic?
What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables
What are advantages of fuzzy logic systems?
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