[amp_mcq option1=”Hill-climbing search” option2=”Hidden markov model” option3=”Depth-first search” option4=”Breadth-first search” correct=”option2″]
The correct answer is B. Hidden Markov model.
A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov chain with unobserved (hidden) states. The model is frequently used for speech recognition, natural language processing, and machine translation.
A hill-climbing search is a local search algorithm that starts at a given state and repeatedly moves to neighboring states that are better according to some criterion. The algorithm terminates when it reaches a local optimum, which is a state that cannot be improved by moving to any of its neighbors.
A depth-first search (DFS) is an algorithm for traversing a graph. The algorithm starts at a given node and recursively explores all of the node’s neighbors. If a node has no neighbors, the algorithm backtracks to the previous node.
A breadth-first search (BFS) is an algorithm for traversing a graph. The algorithm starts at a given node and explores all of the node’s neighbors in a breadth-first order. This means that the algorithm explores the node’s neighbors before exploring any of the node’s neighbors’ neighbors.
In the context of temporal probabilistic reasoning, a hidden Markov model is a more appropriate algorithm than hill-climbing search, depth-first search, or breadth-first search. This is because a hidden Markov model can model the temporal dependencies between the hidden states. Hill-climbing search, depth-first search, and breadth-first search are all local search algorithms that cannot model temporal dependencies.