TheBaum-Welch algorithmis a special case of the Expectation-Maximization (EM) algorithm used to train Hidden Markov Models (HMMs). It estimates model parameters (transition probabilities, emission probabilities) when the training data is incomplete or hidden.
Viterbi algorithmis for decoding, not training.
Forward-backward algorithmis part of Baum-Welch’s expectation step but is not a standalone training method.
Exhaustive searchis not a standard HMM training algorithm.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Baum-Welch algorithm iteratively optimizes HMM parameters using forward and backward probability computations until convergence."
[Reference:HCIP-AI EI Developer V2.5 Official Study Guide – Chapter: HMM Training Algorithms, ]
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