What is Viterbi algorithm in HMM?
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).
What does a Viterbi decoder do?
A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm).
Why do we need Viterbi?
The Viterbi algorithm provides an efficient way of finding the most likely state sequence in the maximum a posteriori probability sense of a process assumed to be a finite-state discrete-time Markov process. Such processes can be subsumed under the general statistical framework of compound decision theory.
Is the Viterbi algorithm greedy?
The Viterbi algorithm is not a greedy algorithm. It performs a global optimisation and guarantees to find the most likely state sequence, by exploring all possible state sequences. An example of a greedy algorithm is the one for training a CART.
What are the three basic problems of HMMs?
HMM provides solution of three problems : evaluation, decoding and learning to find most likelihood classification.
Why is Viterbi algorithm efficient?
In a Hidden Markov Model, the Viterbi algorithm is the right way to find the highest-probability sequence of hidden states x, given your sequence of observations y. It will find an exact solution (not an approximation) in time O(TK2) instead of the O(KT) that you’d suffer with the brute force solution.
What is the Viterbi algorithm for HMM?
In the second article, it was about the training algorithm for HMM. In this one, the focus will be on the prediction algorithm, which is called the Viterbi algorithm. Like Baum-Welch algorithm, our training algorithm, the Viterbi algorithm is also a dynamic programming approach.
Is the Viterbi algorithm a dynamic programming approach?
Like Baum-Welch algorithm, our training algorithm, the Viterbi algorithm is also a dynamic programming approach. As a reminder, the dynamic programming is an approach that you would reuse calculated result in the next calculation, and the act of reusing saves time!
What is iterative Viterbi decoding in machine learning?
With the algorithm called iterative Viterbi decoding one can find the subsequence of an observation that matches best (on average) to a given hidden Markov model. This algorithm is proposed by Qi Wang et al. to deal with turbo code.
What is Viterbi function in NLP?
The function viterbi takes the following arguments: obs is the sequence of observations, e.g. [‘normal’, ‘cold’, ‘dizzy’]; states is the set of hidden states; start_p is the start probability; trans_p are the transition probabilities; and emit_p are the emission probabilities.