Viterbi algorithm

The Viterbi algorithm is a dynamic programming algorithm for obtaining the maximum a posteriori probability estimate of the most likely sequence of hidden states—called the Viterbi path—that results in a sequence of observed events, especially in the context of Markov information sources and hidden Markov models (HMM).

1. What is the main idea in the Viterbi algorithm?
2. What is the output of Viterbi algorithm?
3. What is hidden Markov model in NLP?
4. What is the time complexity of the Viterbi algorithm?

What is the main idea in the Viterbi algorithm?

The main idea behind the Viterbi Algorithm is that we can calculate the values of the term π(k, u, v) efficiently in a recursive, memoized fashion.

What is the output of Viterbi algorithm?

Viterbi (2009), Scholarpedia, 4(1):6246. The Viterbi Algorithm produces the maximum likelihood estimates of the successive states of a finite-state machine (FSM) from the sequence of its outputs which have been corrupted by successively independent interference terms.

What is hidden Markov model in NLP?

Hidden Markov Model (HMM) is a probabilistic graphical model, which allows us to calculate a sequence of unknown or unobserved variables from a set of observed variables. Predicting weather conditions (hidden) on the basis of types of clothes worn by someone (observed) is a simple example of HMM.

What is the time complexity of the Viterbi algorithm?

The time complexity of this algorithm is O(N2T) and the space complexity is O(N2 + NT).