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Simple implementation of Hidden Markov Model for discrete outcomes/observations in Python. It contains implementation of 1. Forward algorithm 2. Viterbi Algorithm and 3. Forward/Backward i.e. Baum-Welch Algorithm.
This repository holds an implementation of Discrete Hidden Markov Model which is trained and tuned to work for the sequence prediction challenge (SPiCe). Parameter tuning of this simple HMM implementation got me top 10 in the global ranking of the Sequence Prediction Challenge(SPiCe).
How to infer the transition probabilities for an HMM and the effects of sampling. This is a complement on some discussion about the follow lecture https://youtu.be/34Noy-7bPAo of the Artificial Intelligence Nano degree from Udacity
Parts of Speech Tagging and Optical Character Recognition using Naive Bayes and Hidden Markov Model(HMM) with Forward-Backward Variable Elimination Algorithm and Viterbi Algorithm