Hidden Markov Models Quiz

What is a Hidden Markov Model (HMM) primarily used for?

• Predicting stock market trends

• Identifying hidden states in sequential data

• Analyzing image pixels

• Calculating genetic mutations

In an HMM, what do the emission probabilities represent?

• The probability of transitioning from one state to another

• The likelihood of observing a certain output given a state

• The initial probabilities of each state

• The total probability of all states

What algorithm is used in HMMs to find the most likely sequence of hidden states given a sequence of observations?

• Baum-Welch Algorithm

• Viterbi Algorithm

• Forward Algorithm

• Backward Algorithm

Which of the following is NOT a component of an HMM?

• Transition matrix

• Emission matrix

• Initial state distribution

• Observation probability matrix

In a first-order HMM, the probability of being in a particular state depends only on the previous state.

In the context of HMMs, the term “state space” refers to the set of all possible observations.

In which type of HMM are observations modeled as continuous variables, typically using a Gaussian distribution?

• Discrete HMM

• Continuous HMM

• Gaussian HMM

• Hidden Markov Model with Multinomial Emissions

In a Left-to-Right HMM, how are state transitions constrained?

• Transitions can occur only from the current state to previous states.

• Transitions can occur only from the current state to future states.

• Transitions can occur only from the current state to any other state.

• Transitions are not allowed between states.

Continuous HMMs are often used in cases where the observations are categorical or nominal.

The observation probabilities in both Ergodic and Left-to-Right HMMs are modeled in the same way, but their state transition structures differ.