Which variable can give the concrete form to the representation of the transition model?

a) Single variable

b) Discrete state variable

c) Random variable

d) Both Single & Discrete state variable

1 Answer

Answer :

d) Both Single & Discrete state variable

Related questions

Description : How does the state of the process is described in HMM? a) Literal b) Single random variable c) Single discrete random variable d) None of the mentioned

Last Answer : c) Single discrete random variable

Description : Which variable cannot be written in entire distribution as a table? a) Discrete b) Continuous c) Both Discrete & Continuous d) None of the mentioned

Last Answer : b) Continuous

Description : A Hybrid Bayesian network contains ___________ a) Both discrete and continuous variables b) Only Discrete variables c) Only Discontinuous variable

Last Answer : a) Both discrete and continuous variables

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

Last Answer : d) Probability distributions for Continuous variables

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

Last Answer : d) Probability distributions for Continuous variables

Description : The values of the set membership is represented by ___________ a) Discrete Set b) Degree of truth c) Probabilities d) Both Degree of truth & Probabilities

Last Answer : b) Degree of truth

Description : The values of the set membership is represented by a) Discrete Set b) Degree of truth c) Probabilities d) Both b & c

Last Answer : b) Degree of truth

Description : What is the basic element of a language? a) Literal b) Variable c) Random variable d) All of the mentioned

Last Answer : c) Random variable

Description : Decision trees are appropriate for the problems where ___________ a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Decision trees are appropriate for the problems where: a) Attributes are both numeric and nominal b) Target function takes on a discrete number of values. c) Data may have errors d) All of the mentioned

Last Answer : d) All of the mentioned

Description : A production rule consists of ____________ a) A set of Rule b) A sequence of steps c) Set of Rule & sequence of steps d) Arbitrary representation to problem

Last Answer : c) Set of Rule & sequence of steps

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

Description : Neural Networks are complex ______________with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : b) Nonlinear Functions

Description : Membership function defines the fuzziness in a fuzzy set irrespective of the elements in the set, which are discrete or continuous. A. True B. False

Last Answer : B. False

Description : Neural Networks are complex ———————–with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions e) Power Functions

Last Answer : b) Nonlinear Functions

Description : Neural Networks are complex ______________ with many parameters. a) Linear Functions b) Nonlinear Functions c) Discrete Functions d) Exponential Functions

Last Answer : a) Linear Functions

Description : Which is omitted in prolog unification algorithm? a) Variable check b) Occur check c) Proposition check d) Both Occur & Proposition check

Last Answer : b) Occur check

Description : is a symbolic representation of discrete elements of information: a. Data b. Code c. Address d. Control

Last Answer : b. Code

Description : The process of removing detail from a given state representation is called ______ a) Extraction b) Abstraction c) Information Retrieval d) Mining of data

Last Answer : b) Abstraction

Description : Explain State space representation of AI with help of Water jug problem.

Last Answer : "You are given two jugs, a 4-gallon one and a 3-gallon one. Neither has any measuring markers on it. There is a tap that can be used to fill the jugs with water. How can you get exactly 2 gallons of water into the 4-gallon jug?". ... the goal state=(2,0) (0,0) (0,3) (3,0) (3,3) (4,2) (0,2) (2,0)

Description : What are the two main features of Genetic Algorithm? a) Fitness function & Crossover techniques b) Crossover techniques & Random mutation c) Individuals among the population & Random mutation d) Random mutation & Fitness function

Last Answer : a) Fitness function & Crossover techniques

Description : Given a sound clip of a person or people speaking, determine the textual representation of the speech. a) Text-to-speech b) Speech-to-text c) All of the mentioned d) None of the mentioned

Last Answer : b) Speech-to-text

Description : Factors which affect the performance of learner system does not include? a) Representation scheme used b) Training scenario c) Type of feedback d) Good data structures

Last Answer : d) Good data structures

Description : Which is not a property of representation of knowledge? a) Representational Verification b) Representational Adequacy c) Inferential Adequacy d) Inferential Efficiency

Last Answer : a) Representational Verification

Description : Which of the factors affect the performance of learner system does not include? a) Representation scheme used b) Training scenario c) Type of feedback d) Good data structures

Last Answer : d) Good data structures

Description : Which of the following is true in Statistical reasoning? a) The representation is extended to allow some kind of numeric measure of certainty to be associated with each statement b) The ... extended to allow some kind of numeric measure of certainty to be associated common to all statements

Last Answer : a) The representation is extended to allow some kind of numeric measure of certainty to be associated with each statement

Description : Consider a good system for the representation of knowledge in a particular domain. What property should it possess? a) Representational Adequacy b) Inferential Adequacy c) Inferential Efficiency d) All of the mentioned

Last Answer : d) All of the mentioned

Description : Which is not a property of representation of knowledge? a) Representational Verification b) Representational Adequacy c) Inferential Adequacy d) Inferential Efficiency

Last Answer : a) Representational Verification

Description : Feature of ANN in which ANN creates its own organization or representation of information it receives during learning time is A. Adaptive Learning B. Self Organization C. What-If Analysis D. Supervised Learniing

Last Answer : B. Self Organization

Description : Factors which affect the performance of learner system does not include a) Representation scheme used b) Training scenario c) Type of feedback d) Good data structures

Last Answer : d) Good data structures

Description : The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ a) Fuzzy Set b) Crisp Set c) Fuzzy & Crisp Set d) None of the mentioned

Last Answer : a) Fuzzy Set

Description : Electronic Discrete Variable Automatic Computer(EDVAC) was designed on __________ concept. (A) Objective Programming (B) Stored program (C) Both (A) and (B) (D) None of the Above

Last Answer : (B) Stored program

Description : A Hybrid Bayesian network contains  Both discrete and continuous variables  Only Discontinuous variable  Both Discrete and Discontinuous variable  Continous variable only.

Last Answer :  Both Discrete and Discontinuous variable

Description : Which are partially captured by triphone model? a) Articulation effects b) Coarticulation effects c) Both Articulation & Coarticulation effects d) None of the mentioned

Last Answer : b) Coarticulation effects

Description : Where does the Hidden Markov Model is used? a) Speech recognition b) Understanding of real world c) Both Speech recognition & Understanding of real world d) None of the mentioned

Last Answer : a) Speech recognition

Description : A network with named nodes and labeled arcs that can be used to represent certain natural language grammars to facilitate parsing. a) Tree Network b) Star Network c) Transition Network d) Complete Network

Last Answer : c) Transition Network

Description : Which of the following will give a more accurate representation of the population from which a sample has been taken? a. A large sample based on the convenience sampling technique b. A small ... simple random sampling c. A large sample based on simple random sampling d. A small cluster sample

Last Answer : c. A large sample based on simple random sampling

Description : Can the mean of a discrete variable like the number of children be normally distributed?

Last Answer : Not really. The distribution is not only discrete but alsoheavily skewed. However, the average number of children in sets offamilies can be approximated by the normal distribution.

Description : EDVAC is 1) Electronic Detected Variable Automatic Computer 2) Electronic Discrete Variable Automatic Computer 3) Electronic Discrete Valuable Automatic Computer 4) Electronic Developed Valuable Automatic Computer

Last Answer : 2) Electronic Discrete Variable Automatic Computer

Description : Define discrete variable by giving examples.

Last Answer : Ans: A variable which can assume only some specific values within a given range is called discrete variable. For e.g. Number of students in a class, Number of houses in a street, number of children in a family etc. it can’t occur in decimal.

Description : Define discrete and continuous variable.

Last Answer : Ans: A variable which can assume only some specific values within a given range is called discrete variable. For e.g. Number of students in a class, Number of houses in a street, number of children in a family etc. ... at a place, income of a person, height of a plant, a life time of a T.V tube etc.

Description : EDVAC is 1 Electronic Detected Variable Automatic Computer 2 Electronic Discrete Variable Automatic Computer 3 Electronic Discrete Valuable Automatic Computer 4 Electronic Developed Valuable Automatic Computer

Last Answer : 2 Electronic Discrete Variable Automatic Computer

Description : The explanation facility of an expert system may be used to ____________ a) construct a diagnostic model b) expedite the debugging process c) explain the system’s reasoning process d) explain the system’s reasoning process & expedite the debugging process

Last Answer : d) explain the system’s reasoning process & expedite the debugging process

Description : The explanation facility of an expert system may be used to __________ a) construct a diagnostic model b) expedite the debugging process c) explain the system’s reasoning process d) expedite the debugging process & explain the system’s reasoning process

Last Answer : d) expedite the debugging process & explain the system’s reasoning process

Description : What is meant by predicting the value of a state variable from the past? a) Specular reflection b) Diffuse reflection c) Gaussian filter d) Smoothing

Last Answer : d) Smoothing

Description : What is state space? a) The whole problem b) Your Definition to a problem c) Problem you design d) Representing your problem with variable and parameter

Last Answer : d) Representing your problem with variable and parameter

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

Last Answer : c) 3

Description : The primitives in probabilistic reasoning are random variables. a) True b) False

Last Answer : a) True

Description : __________ algorithm keeps track of k states rather than just one. a) Hill-Climbing search b) Local Beam search c) Stochastic hill-climbing search d) Random restart hill-climbing search

Last Answer : b) Local Beam search

Description : Stochastic hill climbing chooses at random from among the uphill moves; the probability of selection can vary with the steepness of the uphil1 move. a) True b) False

Last Answer : a) True