Initialization in Viterbi Algorithm
Initialization is the first step of the Viterbi algorithm. It sets up the initial probabilities for the starting states based on the initial state probabilities and the emission probabilities for the first observation.
Mathematically it can be represented as:
[Tex]V_1 (j) = \pi_j. b_j(o_1) \forall j \epsilon \{1, …,N\} [/Tex]
[Tex]\text{Path}_j(1) = [j] \forall j \epsilon \{ 1, …, N \}[/Tex]
Viterbi Algorithm for Hidden Markov Models (HMMs)
The Viterbi algorithm is a dynamic programming algorithm for finding the most likely sequence of hidden states in a Hidden Markov Model (HMM). It is widely used in various applications such as speech recognition, bioinformatics, and natural language processing. This article delves into the fundamentals of the Viterbi algorithm, its applications, and a step-by-step guide to its implementation.
Table of Content
- Understanding Hidden Markov Models (HMMs)
- The Viterbi Algorithm
- Initialization in Viterbi Algorithm
- The Forward Algorithm
- The Backward Algorithm
- Decoding with Viterbi Algorithm
- Optimizing Viterbi Algorithm
- Example: Viterbi Algorithm in Action
- Applications of Viterbi in HMM
- Conclusion