Artificial Intelligence Questions & Answers – Hidden Markov Model

This set of Artificial Intelligence Multiple Choice Questions & Answers (MCQs) focuses on “Hidden Markov Model”.

1. Which algorithm is used for solving temporal probabilistic reasoning?
a) Hill-climbing search
b) Hidden markov model
c) Depth-first search
d) Breadth-first search
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2. 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
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3. What are the possible values of the variable?
a) Variables
b) Literals
c) Discrete variable
d) Possible states of the world
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4. Where does the additional variables are added in HMM?
a) Temporal model
b) Reality model
c) Probability model
d) All of the mentioned
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5. Which allows for a simple and matrix implementation of all the basic algorithm?
a) HMM
b) Restricted structure of HMM
c) Temporary model
d) Reality model
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6. 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
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7. 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
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8. Which algorithm works by first running the standard forward pass to compute?
a) Smoothing
b) Modified smoothing
c) HMM
d) Depth-first search algorithm
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9. Which reveals an improvement in online smoothing?
a) Matrix formulation
b) Revelation
c) HMM
d) None of the mentioned
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10. Which suggests the existence of an efficient recursive algorithm for online smoothing?
a) Matrix
b) Constant space
c) Constant time
d) None of the mentioned
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Sanfoundry Global Education & Learning Series – Artificial Intelligence.