Feat viterbi #232
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Feat viterbi #232
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Pull Request Overview
This PR adds an implementation of the Viterbi Algorithm for Hidden Markov Model (HMM) decoding. The algorithm finds the most probable sequence of hidden states given observed events using dynamic programming.
Key Changes
- Implements the core Viterbi algorithm with initialization, recursion, and backtracking steps
- Provides a complete working example using a weather prediction scenario
- Includes comprehensive documentation explaining the algorithm, complexity, and usage
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
Co-authored-by: Copilot <[email protected]>
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It's already implemented here quantitative_finance/hidden_markov_model.r under a different name. Please correct if they are different |
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This R program implements the Viterbi Algorithm — a dynamic programming method used in Hidden Markov Models (HMMs) to find the most probable sequence of hidden states that could have produced a given sequence of observed events.
It works in three main steps:
Initialization: Calculates initial probabilities for each state using start and emission probabilities.
Recursion: Iteratively updates probabilities for each observation by considering all possible previous states.
Backtracking: Reconstructs the most likely state sequence from the stored backpointers.
Use case: Speech recognition, weather prediction, bioinformatics (e.g., gene sequence analysis).
Time Complexity: O(N × T)
Space Complexity: O(N × T)