# Hidden Markov Model (MCQ)

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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
Explanation: Hidden Markov model is used for solving temporal probabilistic reasoning that was independent of transition and sensor model.

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
Explanation: An HMM is a temporal probabilistic model in which the state of the process is described by a single discrete random variable.

3. What are the possible values of the variable?
a) Variables
b) Literals
c) Discrete variable
d) Possible states of the world
Explanation: The possible values of the variables are the possible states of the world.

a) Temporal model
b) Reality model
c) Probability model
d) All of the mentioned
Explanation: Additional state variables can be added to a temporal model while staying within the HMM framework.

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
Explanation: Restricted structure of HMM allows for a very simple and elegant matrix implementation of all the basic algorithm.

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
Explanation: None.

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
Explanation: With a single, discrete state variable, we can give concrete form to the representation of the transition model.

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
Explanation: The modified smoothing algorithm works by first running the standard forward pass to compute and then running the backward pass.

9. Which reveals an improvement in online smoothing?
a) Matrix formulation
b) Revelation
c) HMM
d) None of the mentioned