Question 1

Fuzzy logic is a form of
A)two-valued logic
B)crisp set logic
C)many-valued logic
D)binary set logic

Accepted Answer

Fuzzy logic is a form of many-valued logic where the truth values of variables may be any real number between 0 and 1, allowing for reasoning in situations that are not simply true or false but rather can be any degree of truth.

Question 2

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

Accepted Answer

In traditional set theory, an element either belongs to a set or does not, represented by the truth values 0 or 1, respectively. In fuzzy set theory, elements can belong to a set to varying degrees, represented by truth values between 0 and 1.

Question 3

How many types of random variables are available?
A)1
B)2
C)3
D)4

Accepted Answer

There are three types of random variables: discrete, continuous, and mixed.

Question 4

The values of the set membership is represented by
A)discrete set
B)degree of truth
C)probabilities
D)both b & c

Accepted Answer

The answer of The values of the set membership is...

Question 5

What is meant by probability density function?
A)probability distributions
B)continuous variable
C)discrete variable
D)probability distributions for continuous variables

Accepted Answer

A probability density function (PDF) is specifically used to describe the probability distributions for continuous variables, indicating the likelihood of a continuous random variable falling within a particular range of values.

Question 6

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

Accepted Answer

Probability theory sentences often use an extension of propositional logic, incorporating probabilistic elements into logical frameworks.

Question 7

Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following.
A)and
B)or
C)not
D)ex-or

Accepted Answer

The answer of Fuzzy Set theory defines fuzzy operators. Choose...

Question 8

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

Accepted Answer

Hedges are linguistic operators used in fuzzy logic to modify the meaning of fuzzy sets, making them more or less fuzzy, and are thus applied to fuzzy set theory to adjust the degree of membership of elements in a set.

Question 9

Where does the Bayes rule can be used?
A)solving queries
B)increasing complexity
C)decreasing complexity
D)answering probabilistic query

Accepted Answer

Bayes' rule is used to answer probabilistic queries by updating the probability estimate for a hypothesis as more evidence or information becomes available.

Question 10

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

Accepted Answer

A Bayesian network provides a complete description of the domain by representing the probabilistic relationships among a set of variables.

Question 11

Fuzzy logic is usually represented as
A)if-then-else rules
B)if-then rules
C)both a & b
D)none of the mentioned

Accepted Answer

Fuzzy logic is typically represented using "if-then" rules, which allow for reasoning with degrees of truth rather than the binary true/false found in traditional logic.

Question 12

______________ is/are the way/s to represent uncertainty.
A)fuzzy logic
B)probability
C)entropy
D)all of the mentioned

Accepted Answer

Fuzzy logic, probability, and entropy are all methods used to represent uncertainty in various contexts, such as in decision-making processes, statistical analysis, and information theory.

Question 13

____________ 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

Accepted Answer

The answer of ____________ are algorithms that learn from their...

Question 14

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

Accepted Answer

A fully connected condition implies that each variable in the system is directly influenced by all other variables, allowing for the most comprehensive interaction among them.

Question 15

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

Accepted Answer

In a Bayesian network, a node is conditionally independent of its predecessors given its parents. This means that once you know the state of a node's parents, the node's state is independent of any other ancestor nodes in the network.

Question 16

How many terms are required for building a Bayesian model?
A)1
B)2
C)3
D)4

Accepted Answer

A Bayesian model requires three terms: prior probability, likelihood, and marginal likelihood. These components are used to calculate the posterior probability, which is the probability of the hypothesis given the observed evidence.

Question 17

What is needed to make probabilistic systems feasible in the world?
A)reliability
B)crucial robustness
C)feasibility
D)none of the mentioned

Accepted Answer

The answer of What is needed to make probabilistic systems...

Question 18

How the entries in the full joint probability distribution can be calculated?
A)using variables
B)using information
C)both a & b
D)none of the mentioned

Accepted Answer

The answer of How the entries in the full joint...

Question 19

How the Bayesian network can be used to answer any query?
A)full distribution
B)joint distribution
C)partial distribution
D)all of the mentioned

Accepted Answer

A Bayesian network represents the joint distribution of a set of variables, allowing it to answer any query about the variables by using this joint distribution.

Question 20

How the compactness of the Bayesian network can be described?
A)locally structured
B)fully structured
C)partial structure
D)all of the mentioned

Accepted Answer

Bayesian networks are locally structured, meaning they represent local dependencies among variables, which allows for a compact representation of the joint probability distribution.

Quiz 1: Fuzzy Logic, Set Theory, and Bayesian Networks

Fuzzy logic is a form of

The truth values of traditional set theory is __ and that of fuzzy set is

How many types of random variables are available?

The values of the set membership is represented by

What is meant by probability density function?

Which of the following is used for probability theory sentences?

Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following.

There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory.

Where does the Bayes rule can be used?

What does the Bayesian network provides?

Fuzzy logic is usually represented as

______________ is/are the way/s to represent uncertainty.

____________ are algorithms that learn from their more complex environments (hence eco) to generalize, approximate and simplify solution logic.

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

What is the consequence between a node and its predecessors while creating Bayesian network?

How many terms are required for building a Bayesian model?

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

How the entries in the full joint probability distribution can be calculated?

How the Bayesian network can be used to answer any query?

How the compactness of the Bayesian network can be described?